Publications
Journal Article
Artificial intelligence in dry eye disease
The Ocular Surface 23 (2022): 74-86.Status: Published
Artificial intelligence in dry eye disease
Dry eye disease (DED) has a prevalence of between 5 and 50%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term ‘AI’ is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of interferometry, slit-lamp and meibography images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | The Ocular Surface |
| Volume | 23 |
| Pagination | 74 - 86 |
| Date Published | Jan-01-2022 |
| Publisher | Elsevier |
| ISSN | 15420124 |
| Keywords | artificial intelligence, Dry eye disease, Machine learning |
| URL | https://linkinghub.elsevier.com/retrieve/pii/S1542012421001324 |
| DOI | 10.1016/j.jtos.2021.11.004 |
Automatic Tracking of the ICSI procedure using Deep Learning
Human Reproduction 37, no. Supplement_1 (2022).Status: Published
Automatic Tracking of the ICSI procedure using Deep Learning
Study question
Can deep learning be used to detect and track spermatozoa and the different parts of an ICSI procedure?
Summary answer
Deep learning can be used as a tool to assist and organize the contents of an ICSI procedure.
What is known already
Sperm tracking has been a topic of research and practice for many years, especially in the context of computer-aided sperm analysis (CASA). Recent studies have proposed using deep learning algorithms to track spermatozoa for spermatozoon selection in human and animal samples. One critical part of performing ICSI involves the selection of the “best” spermatozoon for injection, but other parts of the procedure may also be of importance. However, as far as we know, tracking using deep learning has not been applied to the ICSI procedure, where detecting instruments and the oocyte could also be helpful in post-analysis and training.
Study design, size, duration
The study was performed using three anonymized videos of the ICSI procedure. The frames of the videos were manually annotated by data scientists and verified by an embryologist. The annotations were bounding boxes around specific parts of the ICSI procedure, including sperm, pipettes, and the oocyte. We trained a YOLOv5 model on the collected data, where two videos were used for training and one video for validation.
Participants/materials, setting, methods
The videos of the ICSI procedure were captured at 200x magnification with a DeltaPix camera at Fertilitetssenteret in Oslo, Norway. ICSI was performed using a Nikon ECLIPSE TE2000-S microscope connected with Eppendorf TransferMan 4m micromanipulators. The spermatozoa were immobilised in 5 µl Polyvinylpyrrolidone (PVP; CooperSurgical). The videos had a resolution of 1920x1080 and were resized to 640x640 before being processed by the YOLOv5 model. The data will be made public in a later study.
Main results and the role of chance
Mean average precision (mAP) with the threshold of 0.5 (mAP@.5) is the main quantitative parameter measured in the YOLOv5 model. All the experiments were performed using three-fold cross-validation, where we present the average metrics calculated over the three folds. Overall, the method showed an average mAP@.5 of 0.50 across all predicted classes, which means that the method can track the different components with good accuracy. Looking closer at the individual classes, we see that instruments like the holding pipette and ICSI pipette are detected with high accuracy with a mAP@.5 of 0.87 and 0.94, respectively. The oocyte is also easily tracked with a mAP@.5 of 0.92. The first polar body is well detected with a mAP@.5 of 0.65. The model has issues detecting and tracking individual sperm (both outside and within the pipette), where the method achieved a mAP@.5 of 0.46 for tracking sperm outside the pipette and 0.03 for the sperm inside the pipette. The low score of detecting the sperm in the pipette can be explained by the often unclear visibility of the sperm through the pipette and the low number of training samples.
Limitations, reasons for caution
The limited sample size makes the generalizability of the method difficult to determine. A more extensive evaluation is necessary. Moreover, as the currency study focuses on tracking, patient information and clinical outcome were not included in the analysis.
Wider implications of the findings
Deep learning has the potential to aid embryologists to perform successful ICSI through tracking and detection of spermatozoa, pipettes, and the oocyte. This could potentially lead to better internal quality control and teaching possibilities, and hopefully better results.
Trial registration number
not applicable
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Human Reproduction |
| Volume | 37 |
| Issue | Supplement_1 |
| Date Published | 07/2022 |
| Publisher | Oxford University Press |
| ISSN | 0268-1161 |
| URL | https://academic.oup.com/humrep/article/37/Supplement_1/deac107.261/6619904 |
| DOI | 10.1093/humrep/deac107.261 |
Automating tracking of cell division for human embryo development in time lapse videos
Human Reproduction (2022).Status: Accepted
Automating tracking of cell division for human embryo development in time lapse videos
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Human Reproduction |
| Publisher | Human Reproduction |
Bias i kvantitativ analyse innen velferd
Tidsskrift for velferdsforskning (2022).Status: Accepted
Bias i kvantitativ analyse innen velferd
Ifølge Norges nasjonale strategi for kunstig intelligens (2020) er offentlig forvaltning og helse blant Norges satsningsområder for bruk av kunstig intelligens. Maskinlæring er en undergruppe av kunstig intelligens med potensiale for å løse en rekke utfordringer, men som også gir opphav til utfordringer. En slik utfordring er bias, eller skjevhet. Et eksempel på skjevhet er at tilstedeværende ulikheter i samfunnet representeres i datagrunnlaget maskinlæringsmodeller utvikles på. De resulterende modellene står dermed i fare for å adoptere og videreføre disse ulikhetene. En utfordring er at skjevhet har ulike definisjoner innen ulike fagområder, og kan ha mange ulike opphav. Vi bidrar til å løse denne utfordringen ved å gi en oversikt over ulike typer skjevhet og deres opphav med illustrasjoner fra et velferdsperspektiv, samt avklarer forskjellen til det nærliggende konseptet rettferdighet. Vi demonstrerer utfordringer relatert til databaserte modellers oppførsel ved å benytte maskinlæring til å predikere fremtidig ressursbehov i helsevesenet, spesifikt antall legebesøk i kommuner. Vi demonstrerer ulike typer skjevheter, diskuterer mulige løsninger og bruker metoder fra forklarbar kunstig intelligens for å analysere opphavet til skjevheter i forklaringsvariablene. Det finnes ingen universell løsning for å håndtere alle typer skjevheter, men skjevhet må tas høyde for i alle deler av en kvantitativ analyse.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Tidsskrift for velferdsforskning |
| Publisher | Universitetsforlaget |
| Place Published | Tidsskrift for velferdsforskning |
| Keywords | forklarbar kunstig intelligens, Maskinlæring, skjevhetsbegreper, velferdsforskning, XGBoost |
Deep Tower Networks for Efficient Temperature Forecasting from Multiple Data Sources
Sensors 2232, no. 7 (2022): 2802.Status: Published
Deep Tower Networks for Efficient Temperature Forecasting from Multiple Data Sources
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Sensors |
| Volume | 2232 |
| Issue | 7 |
| Pagination | 2802 |
| Date Published | 01-04-2022 |
| Publisher | MPDI |
| URL | https://www.mdpi.com/1424-8220/22/7/2802https://www.mdpi.com/1424-8220/2... |
| DOI | 10.3390/s22072802 |
Efficient quantile tracking using an oracle
Applied Intelligence (2022): 1-12.Status: Published
Efficient quantile tracking using an oracle
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Applied Intelligence |
| Pagination | 1–12 |
| Publisher | Springer |
Prediction of Schizophrenia from Activity Data using Hidden Markov Model Parameters
Neural Computing and Applications (2022).Status: Published
Prediction of Schizophrenia from Activity Data using Hidden Markov Model Parameters
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Neural Computing and Applications |
| Publisher | Springer |
Real-time deep learning based multi object tracking of spermatozoa in fresh samples
Human Reproduction 37, no. Supplement_1, July 2022, deac107.104 (2022).Status: Published
Real-time deep learning based multi object tracking of spermatozoa in fresh samples
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Human Reproduction |
| Volume | 37 |
| Issue | Supplement_1, July 2022, deac107.104 |
| Date Published | 06/2022 |
| Publisher | Human Reproduction |
| DOI | 10.1093/humrep/deac107.104 |
SinGAN-Seg: Synthetic training data generation for medical image segmentation
PLOS ONE (2022).Status: Published
SinGAN-Seg: Synthetic training data generation for medical image segmentation
Analyzing medical data to find abnormalities is a time-consuming and costly task, particularly for rare abnormalities, requiring tremendous efforts from medical experts. Therefore, artificial intelligence has become a popular tool for the automatic processing of medical data, acting as a supportive tool for doctors. However, the machine learning models used to build these tools are highly dependent on the data used to train them. Large amounts of data can be difficult to obtain in medicine due to privacy reasons, expensive and time-consuming annotations, and a general lack of data samples for infrequent lesions. In this study, we present a novel synthetic data generation pipeline, called SinGAN-Seg, to produce synthetic medical images with corresponding masks using a single training image. Our method is different from the traditional generative adversarial networks (GANs) because our model needs only a single image and the corresponding ground truth to train. We also show that the synthetic data generation pipeline can be used to produce alternative artificial segmentation datasets with corresponding ground truth masks when real datasets are not allowed to share. The pipeline is evaluated using qualitative and quantitative comparisons between real data and synthetic data to show that the style transfer technique used in our pipeline significantly improves the quality of the generated data and our method is better than other state-of-the-art GANs to prepare synthetic images when the size of training datasets are limited. By training UNet++ using both real data and the synthetic data generated from the SinGAN-Seg pipeline, we show that the models trained on synthetic data have very close performances to those trained on real data when both datasets have a considerable amount of training data. In contrast, we show that synthetic data generated from the SinGAN-Seg pipeline improves the performance of segmentation models when training datasets do not have a considerable amount of data. All experiments were performed using an open dataset and the code is publicly available on GitHub.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | PLOS ONE |
| Date Published | 05/2022 |
| Publisher | PLOS.org |
| URL | https://doi.org/10.1371/journal.pone.0267976 |
| DOI | 10.1371/journal.pone.0267976 |
Proceedings, refereed
Automatic Unsupervised Clustering of Videos of the Intracytoplasmic Sperm Injection (ICSI) Procedure
In NAIS 2022. NAIS 2022, 2022.Status: Accepted
Automatic Unsupervised Clustering of Videos of the Intracytoplasmic Sperm Injection (ICSI) Procedure
The in vitro fertilization procedure called intracytoplasmic sperm injection can be used to help fertilize an egg by injecting a single sperm cell directly into the cytoplasm of the egg. In order to evaluate, refine and improve the method in the fertility clinic, the procedure is usually observed at the clinic. Alternatively, a video of the procedure can be examined and labeled in a time-consuming process. To reduce the time required for the assessment, we propose an unsupervised method that automatically clusters video frames of the intracytoplasmic sperm injection procedure. Deep features are extracted from the video frames and form the basis for a clustering method. The method provides meaningful clusters representing different stages of the intracytoplasmic sperm injection procedure. The clusters can lead to more efficient examinations and possible new insights that can improve clinical practice. Further on, it may also contribute to improved clinical outcomes due to increased understanding about the technical aspects and better results of the procedure. Despite promising results, the proposed method can be further improved by increasing the amount of data and exploring other types of features.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2022 |
| Conference Name | NAIS 2022 |
| Publisher | NAIS 2022 |
| Keywords | clustering, Computer Vision and Pattern Recognition (cs.CV), human reproduction, medical videos, Unsupervised learning |
Detecting human embryo cleavage stages using YOLO v5 object detection algorithm
In NAIS. NAIS: NAIS, 2022.Status: Accepted
Detecting human embryo cleavage stages using YOLO v5 object detection algorithm
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2022 |
| Conference Name | NAIS |
| Publisher | NAIS |
| Place Published | NAIS |
Journal Article
Artificial intelligence in the fertility clinic: status, pitfalls and possibilities
Human Reproduction 36, no. 9 (2021): 2429-2442.Status: Published
Artificial intelligence in the fertility clinic: status, pitfalls and possibilities
In recent years, the amount of data produced in the field of ART has increased exponentially. The diversity of data is large, ranging from videos to tabular data. At the same time, artificial intelligence (AI) is progressively used in medical practice and may become a promising tool to improve success rates with ART. AI models may compensate for the lack of objectivity in several critical procedures in fertility clinics, especially embryo and sperm assessments. Various models have been developed, and even though several of them show promising performance, there are still many challenges to overcome. In this review, we present recent research on AI in the context of ART. We discuss the strengths and weaknesses of the presented methods, especially regarding clinical relevance. We also address the pitfalls hampering successful use of AI in the clinic and discuss future possibilities and important aspects to make AI truly useful for ART.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | Human Reproduction |
| Volume | 36 |
| Issue | 9 |
| Pagination | 2429 - 2442 |
| Date Published | 07/2021 |
| Publisher | Oxford Academic |
| ISSN | 0268-1161 |
| URL | https://academic.oup.com/humrep/article/36/9/2429/6330662 |
| DOI | 10.1093/humrep/deab168 |
DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine
Nature Scientific Reports 11 (2021): 21896.Status: Published
DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine
Recent global developments underscore the prominent role big data have in modern medical science. But privacy issues constitute a prevalent problem for collecting and sharing data between researchers. However, synthetic data generated to represent real data carrying similar information and distribution may alleviate the privacy issue. In this study, we present generative adversarial networks (GANs) capable of generating realistic synthetic DeepFake 10-s 12-lead electrocardiograms (ECGs). We have developed and compared two methods, named WaveGAN* and Pulse2Pulse. We trained the GANs with 7,233 real normal ECGs to produce 121,977 DeepFake normal ECGs. By verifying the ECGs using a commercial ECG interpretation program (MUSE 12SL, GE Healthcare), we demonstrate that the Pulse2Pulse GAN was superior to the WaveGAN* to produce realistic ECGs. ECG intervals and amplitudes were similar between the DeepFake and real ECGs. Although these synthetic ECGs mimic the dataset used for creation, the ECGs are not linked to any individuals and may thus be used freely. The synthetic dataset will be available as open access for researchers at OSF.io and the DeepFake generator available at the Python Package Index (PyPI) for generating synthetic ECGs. In conclusion, we were able to generate realistic synthetic ECGs using generative adversarial neural networks on normal ECGs from two population studies, thereby addressing the relevant privacy issues in medical datasets.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | Nature Scientific Reports |
| Volume | 11 |
| Pagination | 21896 |
| Date Published | 09/2021 |
| Publisher | Springer nature |
| URL | https://www.nature.com/articles/s41598-021-01295-2 |
| DOI | 10.1038/s41598-021-01295-2 |
Enhanced Equivalence Projective Simulation: A Framework for Modeling Formation of Stimulus Equivalence Classes
Neural Computation 33, no. 2 (2021): 483-527.Status: Published
Enhanced Equivalence Projective Simulation: A Framework for Modeling Formation of Stimulus Equivalence Classes
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | Neural Computation |
| Volume | 33 |
| Issue | 2 |
| Pagination | 483–527 |
| Publisher | MIT Press |
| DOI | 10.1162/neco_a_01346 |
Prediction of Cloud Fractional Cover Using Machine Learning
Big Data and Cognitive Computing 5, no. 4 (2021): 62.Status: Published
Prediction of Cloud Fractional Cover Using Machine Learning
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | Big Data and Cognitive Computing |
| Volume | 5 |
| Issue | 4 |
| Pagination | 62 |
| Date Published | Jan-12-2021 |
| Publisher | MDPI |
| URL | https://www.mdpi.com/2504-2289/5/4/62https://www.mdpi.com/2504-2289/5/4/... |
| DOI | 10.3390/bdcc5040062 |
Towards better explainable deep learning models for embryo selection in ART
Human Reproduction 36, no. Supplement_1 (2021).Status: Published
Towards better explainable deep learning models for embryo selection in ART
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | Human Reproduction |
| Volume | 36 |
| Issue | Supplement_1 |
| Date Published | Jun-08-2021 |
| Publisher | Oxford Academic |
| ISSN | 0268-1161 |
| URL | https://academic.oup.com/humrep/article/doi/10.1093/humrep/deab130.259 |
| DOI | 10.1093/humrep/deab130.259 |
Unraveling the Impact of Land Cover Changes on Climate using Machine Learning and Explainable Artificial Intelligence
Big Data and Cognitive Computing 5 (2021): 55.Status: Published
Unraveling the Impact of Land Cover Changes on Climate using Machine Learning and Explainable Artificial Intelligence
A general issue in climate science is to handle big data and run complex and computationally heavy simulations. In this paper, we explore the potential of using machine learning (ML) to spare computational time and optimize data usage. The paper analyzes the effects of changes in land cover (LC), such as deforestation or urbanization, on local climate. Along with green house gas emission, LC changes are known to be important causes of climate change. ML methods were trained to learn the relation between LC changes and temperature changes. The results showed that Random Forest (RF) outperformed other ML methods. Explainable Artificial Intelligence (XAI) was further used to interpret the RF method and explain the impact of different LC changes on temperature. The results mainly agree with the climate science literature, demonstrating that ML methods in combination with XAI can be useful in analyzing the climate effects of LC changes. All parts of the analysis pipeline are explained including data pre-processing, feature extraction, ML training, performance evaluation and XAI.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | Big Data and Cognitive Computing |
| Volume | 5 |
| Number | 4 |
| Pagination | 55 |
| Publisher | Multidisciplinary Digital Publishing Institute |
Proceedings, refereed
DeepSynthBody: the beginning of the end for data deficiency in medicine
In The International Conference on Applied Artificial Intelligence (ICAPAI). IEEE, 2021.Status: Published
DeepSynthBody: the beginning of the end for data deficiency in medicine
Limited access to medical data is a barrier on developing new and efficient machine learning solutions in medicine such as computer-aided diagnosis, risk assessments, predicting optimal treatments and home-based personal healthcare systems. This paper presents DeepSynthBody: a novel framework that overcomes some of the inherent restrictions and limitations of medical data by using deep generative adversarial networks to produce synthetic data with characteristics similar to the real data, so-called DeepSynth (deep synthetic) data. We show that DeepSynthBody can address two key issues commonly associated with medical data, namely privacy concerns (as a result of data protection rules and regulations) and the high costs of annotations. To demonstrate the full pipeline of applying DeepSynthBody concepts and user-friendly functionalities, we also describe a synthetic medical dataset generated and published using our framework. DeepSynthBody opens a new era of machine learning applications in medicine with a synthetic model of the human body.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2021 |
| Conference Name | The International Conference on Applied Artificial Intelligence (ICAPAI) |
| Publisher | IEEE |
| DOI | 10.1109/ICAPAI49758.2021.9462062 |
HTAD: A Home-Tasks Activities Dataset with Wrist-accelerometer and Audio Features
In 27th International Conference on Multimedia Modeling. Springer, 2021.Status: Published
HTAD: A Home-Tasks Activities Dataset with Wrist-accelerometer and Audio Features
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2021 |
| Conference Name | 27th International Conference on Multimedia Modeling |
| Pagination | 196-205 |
| Publisher | Springer |
| Keywords | Accelerometer, Activity recognition, Audio, dataset, Sensor fusion |
HYPERAKTIV: An Activity Dataset from Patients with Attention-Deficit/Hyperactivity Disorder (ADHD)
In Proceedings of the 12th ACM Multimedia Systems Conference (MMSys '21). ACM, 2021.Status: Published
HYPERAKTIV: An Activity Dataset from Patients with Attention-Deficit/Hyperactivity Disorder (ADHD)
Machine learning research within healthcare frequently lacks the public data needed to be fully reproducible and comparable. Datasets are often restricted due to privacy concerns and legal requirements that come with patient-related data. Consequentially, many algorithms and models get published on the same topic without a standard benchmark to measure against. Therefore, this paper presents HYPERAKTIV, a public dataset containing health, activity, and heart rate data from patients diagnosed with attention deficit hyperactivity disorder, better known as ADHD. The dataset consists of data collected from 51 patients with ADHD and 52 clinical controls. In addition to the activity and heart rate data, we also include a series of patient attributes such as their age, sex, and information about their mental state, as well as output data from a computerized neuropsychological test. Together with the presented dataset, we also provide baseline experiments using traditional machine learning algorithms to predict ADHD based on the included activity data. We hope that this dataset can be used as a starting point for computer scientists who want to contribute to the field of mental health, and as a common benchmark for future work in ADHD analysis.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2021 |
| Conference Name | Proceedings of the 12th ACM Multimedia Systems Conference (MMSys '21) |
| Pagination | 314–319 |
| Publisher | ACM |
| URL | https://dl.acm.org/doi/10.1145/3458305.3478454 |
| DOI | 10.1145/3458305.3478454 |
Identification of spermatozoa by unsupervised learning from video data
In 37th Virtual Annual Meeting of the European Society of Human Reproduction and Embryology (ESHRE). Oxford University Press, 2021.Status: Published
Identification of spermatozoa by unsupervised learning from video data
Identification of individual sperm is essential to assess a given sperm sample's motility behavior. Existing computer-aided systems need training data based on annotations by professionals, which is resource demanding. On the other hand, data analysed by unsupervised machine learning algorithms can improve supervised algorithms that are more stable for clinical applications. Therefore, unsupervised sperm identification can improve computer-aided sperm analysis systems predicting different aspects of sperm samples. Other possible applications are assessing kinematics and counting of spermatozoa.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2021 |
| Conference Name | 37th Virtual Annual Meeting of the European Society of Human Reproduction and Embryology (ESHRE) |
| Publisher | Oxford University Press |
Medico Multimedia Task at MediaEval 2021: Transparency in Medical Image Segmentation
In Mediaeval Medico 2021. Mediaeval 2021, 2021.Status: Published
Medico Multimedia Task at MediaEval 2021: Transparency in Medical Image Segmentation
The Medico Multimedia Task focuses on providing multimedia researchers with the opportunity to contribute to different areas of medicine using multimedia data to solve several subtasks. This year, the task focuses on transparency within machine learning-based medical segmentation systems, where the use case is gastrointestinal endoscopy. In this paper, we motivate the organization of this task, describe the development and test dataset, and present the evaluation process used to assess the participants' submissions.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2021 |
| Conference Name | Mediaeval Medico 2021 |
| Publisher | Mediaeval 2021 |
| URL | https://2021.multimediaeval.com/ |
Temperature Forecasting using Tower Networks
In Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval. New York, NY, USA: Association for Computing Machinery, 2021.Status: Published
Temperature Forecasting using Tower Networks
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2021 |
| Conference Name | Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval |
| Pagination | 18-23 |
| Date Published | 08/2021 |
| Publisher | Association for Computing Machinery |
| Place Published | New York, NY, USA |
| ISBN Number | 9781450385299 |
| URL | https://dl.acm.org/doi/proceedings/10.1145/3463944https://dl.acm.org/doi... |
| DOI | 10.1145/346394410.1145/3463944.3469099 |
Journal Article
A neuro-inspired general framework for the evolution of stochastic dynamical systems: Cellular automata, random Boolean networks and echo state networks towards criticality
Cognitive Neurodynamics 14 (2020): 1-18.Status: Published
A neuro-inspired general framework for the evolution of stochastic dynamical systems: Cellular automata, random Boolean networks and echo state networks towards criticality
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems, No Simula project |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | Cognitive Neurodynamics |
| Volume | 14 |
| Pagination | 1–18 |
| Publisher | Springer |
| URL | https://doi.org/10.1007/s11571-020-09600-x |
Achieving Fair Load Balancing by Invoking a Learning Automata-Based Two-Time-Scale Separation Paradigm
IEEE Transactions on Neural Networks and Learning Systems (2020): 1-14.Status: Published
Achieving Fair Load Balancing by Invoking a Learning Automata-Based Two-Time-Scale Separation Paradigm
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | IEEE Transactions on Neural Networks and Learning Systems |
| Pagination | 1-14 |
| Publisher | IEEE |
An Extensive Study on Cross-Dataset Bias and Evaluation Metrics Interpretation for Machine Learning applied to Gastrointestinal Tract Abnormality Classification
ACM Transactions on Computing for Healthcare 1 (2020): 1-29.Status: Published
An Extensive Study on Cross-Dataset Bias and Evaluation Metrics Interpretation for Machine Learning applied to Gastrointestinal Tract Abnormality Classification
Precise and efficient automated identification of Gastrointestinal (GI) tract diseases can help doctors treat more patients and improve the rate of disease detection and identification. Currently, automatic analysis of diseases in the GI tract is a hot topic in both computer science and medical-related journals. Nevertheless, the evaluation of such an automatic analysis is often incomplete or simply wrong. Algorithms are often only tested on small and biased datasets, and cross-dataset evaluations are rarely performed. A clear understanding of evaluation metrics and machine learning models with cross datasets is crucial to bring research in the field to a new quality level. Towards this goal, we present comprehensive evaluations of five distinct machine learning models using Global Features and Deep Neural Networks that can classify 16 different key types of GI tract conditions, including pathological findings, anatomical landmarks, polyp removal conditions, and normal findings from images captured by common GI tract examination instruments. Inour evaluation, we introduce performance hexagons using six performance metrics such as recall, precision, specificity, accuracy, F1-score, and Matthews Correlation Coefficient to demonstrate how to determine the real capabilities of models rather than evaluatingthem shallowly. Furthermore, we perform cross-dataset evaluations using different datasets for training and testing. With these cross-dataset evaluations, we demonstrate the challenge of actually building a generalizable model that could be used across different hospitals. Our experiments clearly show that more sophisticated performance metrics and evaluation methods need to be applied to get reliable models rather than depending on evaluations of the splits of the same dataset, i.e., the performance metrics should always be interpreted together rather than relying on a single metric.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | ACM Transactions on Computing for Healthcare |
| Volume | 1 |
| Number | 3 |
| Pagination | 1-29 |
| Publisher | ACM |
Balanced difficulty task finder: an adaptive recommendation method for learning tasks based on the concept of state of flow
Cognitive Neurodynamics 14 (2020): 675-687.Status: Published
Balanced difficulty task finder: an adaptive recommendation method for learning tasks based on the concept of state of flow
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | Cognitive Neurodynamics |
| Volume | 14 |
| Number | 5 |
| Pagination | 675–687 |
| Publisher | Springer |
Dyadic Aggregated Autoregressive Model (DASAR) for Automatic Modulation Classification
IEEE Access 8 (2020): 156096-156103.Status: Published
Dyadic Aggregated Autoregressive Model (DASAR) for Automatic Modulation Classification
| Afilliation | Scientific Computing |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | IEEE Access |
| Volume | 8 |
| Pagination | 156096–156103 |
| Publisher | IEEE |
Equivalence Projective Simulation as a Framework for Modeling Formation of Stimulus Equivalence Classes
Neural Computation 32 (2020): 912-968.Status: Published
Equivalence Projective Simulation as a Framework for Modeling Formation of Stimulus Equivalence Classes
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | Neural Computation |
| Volume | 32 |
| Number | 5 |
| Pagination | 912–968 |
| Publisher | MIT Press |
| Place Published | Cambridge, MA, USA |
| ISSN | 0899-7667 |
| URL | https://doi.org/10.1162/neco_a_01274 |
| DOI | 10.1162/neco_a_01274 |
Game-Theoretic Learning for Sensor Reliability Evaluation Without Knowledge of the Ground Truth
IEEE Transactions on Cybernetics (2020).Status: Published
Game-Theoretic Learning for Sensor Reliability Evaluation Without Knowledge of the Ground Truth
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | IEEE Transactions on Cybernetics |
| Publisher | IEEE |
HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy
Scientific Data 7, no. 1 (2020): 1-14.Status: Published
HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy
Artificial intelligence is currently a hot topic in medicine. However, medical data is often sparse and hard to obtain due to legal restrictions and lack of medical personnel for the cumbersome and tedious process to manually label training data. These constraints make it difficult to develop systems for automatic analysis, like detecting disease or other lesions. In this respect, this article presents HyperKvasir, the largest image and video dataset of the gastrointestinal tract available today. The data is collected during real gastro- and colonoscopy examinations at Bærum Hospital in Norway and partly labeled by experienced gastrointestinal endoscopists. The dataset contains 110,079 images and 374 videos, and represents anatomical landmarks as well as pathological and normal findings. The total number of images and video frames together is around 1 million. Initial experiments demonstrate the potential benefits of artificial intelligence-based computer-assisted diagnosis systems. The HyperKvasir dataset can play a valuable role in developing better algorithms and computer-assisted examination systems not only for gastro- and colonoscopy, but also for other fields in medicine.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | Scientific Data |
| Volume | 7 |
| Issue | 1 |
| Pagination | 1-14 |
| Date Published | 08/2020 |
| Publisher | Springer Nature |
| Keywords | dataset, GI, Machine learning |
| URL | http://www.nature.com/articles/s41597-020-00622-y |
| DOI | 10.1038/s41597-020-00622-y |
Mitigating DDoS using weight-based geographical clustering
Concurrency and Computation: Practice and Experience 32 (2020): e5679.Status: Published
Mitigating DDoS using weight-based geographical clustering
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | Concurrency and Computation: Practice and Experience |
| Volume | 32 |
| Number | 11 |
| Pagination | e5679 |
| Publisher | Wiley Online Library |
Solving Sensor Identification Problem Without Knowledge of the Ground Truth Using Replicator Dynamics
IEEE Transactions on Cybernetics (2020): 1-9.Status: Published
Solving Sensor Identification Problem Without Knowledge of the Ground Truth Using Replicator Dynamics
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | IEEE Transactions on Cybernetics |
| Pagination | 1-9 |
| Publisher | IEEE |
Proceedings, refereed
ACM Multimedia BioMedia 2020 Grand Challenge Overview
In Proceedings of the 28th ACM International Conference on Multimedia. New York, NY, USA: Association for Computing Machinery, 2020.Status: Published
ACM Multimedia BioMedia 2020 Grand Challenge Overview
The BioMedia 2020 ACM Multimedia Grand Challenge is the second in a series of competitions focusing on the use of multimedia for different medical use-cases. In this year's challenge, participants are asked to develop algorithms that automatically predict the quality of a given human semen sample using a combination of visual, patient-related, and laboratory-analysis-related data. Compared to last year's challenge, participants are provided with a fully multimodal dataset (videos, analysis data, study participant data) from the field of assisted human reproduction. The tasks encourage the use of the different modalities contained within the dataset and finding smart ways of how they may be combined to further improve prediction accuracy. For example, using only video data or combining video data and patient-related data. The ground truth was developed through a preliminary analysis done by medical experts following the World Health Organization's standard for semen quality assessment. The task lays the basis for automatic, real-time support systems for artificial reproduction. We hope that this challenge motivates multimedia researchers to explore more medical-related applications and use their vast knowledge to make a real impact on people's lives.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2020 |
| Conference Name | Proceedings of the 28th ACM International Conference on Multimedia |
| Pagination | 4655–4658 |
| Publisher | Association for Computing Machinery |
| Place Published | New York, NY, USA |
| ISBN Number | 9781450379885 |
| Keywords | artificial intelligence, Machine learning, male fertility, semen analysis, spermatozoa |
| URL | https://doi.org/10.1145/3394171.3416287 |
| DOI | 10.1145/3394171.3416287 |
Analysis of Optical Brain Signals Using Connectivity Graph Networks
In International Cross-Domain Conference for Machine Learning and Knowledge Extraction. Springer, 2020.Status: Published
Analysis of Optical Brain Signals Using Connectivity Graph Networks
| Afilliation | Scientific Computing |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2020 |
| Conference Name | International Cross-Domain Conference for Machine Learning and Knowledge Extraction |
| Pagination | 485–497 |
| Publisher | Springer |
EvoDynamic: A Framework for the Evolution of Generally Represented Dynamical Systems and Its Application to Criticality
In International Conference on the Applications of Evolutionary Computation (Part of EvoStar). Cham: Springer, 2020.Status: Published
EvoDynamic: A Framework for the Evolution of Generally Represented Dynamical Systems and Its Application to Criticality
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems, No Simula project |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2020 |
| Conference Name | International Conference on the Applications of Evolutionary Computation (Part of EvoStar) |
| Pagination | 133–148 |
| Publisher | Springer |
| Place Published | Cham |
PMData: a sports logging dataset
In Proceedings of the 11th ACM Multimedia Systems Conference. ACM, 2020.Status: Published
PMData: a sports logging dataset
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2020 |
| Conference Name | Proceedings of the 11th ACM Multimedia Systems Conference |
| Pagination | 231-236 |
| Publisher | ACM |
The EndoTect 2020 Challenge: Evaluation and Comparison of Classification, Segmentation and Inference Time for Endoscopy
In 25th International Conference on Pattern Recognition (ICPR). IEEE, 2020.Status: Published
The EndoTect 2020 Challenge: Evaluation and Comparison of Classification, Segmentation and Inference Time for Endoscopy
The EndoTect challenge at the International Conference on Pattern Recognition 2020 aims to motivate the development of algorithms that aid medical experts in finding anomalies that commonly occur in the gastrointestinal tract. Using HyperKvasir, a large dataset containing images taken from several endoscopies, the participants competed in three tasks. Each task focuses on a specific requirement for making it useful in a real-world medical scenario. The tasks are (i) high classification performance in terms of prediction accuracy, (ii) efficient classification measured by the number of images classified per second, and (iii) pixel-level segmentation of specific anomalies. Hopefully, this can motivate different computer science researchers to help benchmark a crucial component of a future computer-aided diagnosis system, which in turn, could potentially save human lives.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2020 |
| Conference Name | 25th International Conference on Pattern Recognition (ICPR) |
| Publisher | IEEE |
Toadstool: A Dataset for Training Emotional IntelligentMachines Playing Super Mario Bros
In The ACM Multimedia Systems Conference (MMSys). The ACM Multimedia Systems Conference (MMSys): ACM, 2020.Status: Published
Toadstool: A Dataset for Training Emotional IntelligentMachines Playing Super Mario Bros
Games are often defined as engines of experience, and they are heavily relying on emotions, they arouse in players. In this paper, we present a dataset called Toadstool as well as a reproducible methodology to extend on the dataset. The dataset consists of video, sensor, and demographic data collected from ten participants playing Super Mario Bros, an iconic and famous video game. The sensor data is collected through an Empatica E4 wristband, which provides high-quality measurements and is graded as a medical device. In addition to the dataset and the methodology for data collection, we present a set of baseline experiments which show that we can use video game frames together with the facial expressions to predict the blood volume pulse of the person playing Super Mario Bros. With the dataset and the collection methodology we aim to contribute to research on emotionally aware machine learning algorithms, focusing on reinforcement learning and multimodal data fusion. We believe that the presented dataset can be interesting for a manifold of researchers to explore exciting new interdisciplinary questions.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2020 |
| Conference Name | The ACM Multimedia Systems Conference (MMSys) |
| Publisher | ACM |
| Place Published | The ACM Multimedia Systems Conference (MMSys) |
| URL | https://dl.acm.org/doi/10.1145/3339825.3394939 |
| DOI | 10.1145/3339825.3394939 |
Poster
Big data is not always better – prediction of live birth using machine learning on time-lapse videos of human embryos
ESHRE virtual 36th Annual Meeting: ESHRE, 2020.Status: Published
Big data is not always better – prediction of live birth using machine learning on time-lapse videos of human embryos
| Afilliation | Machine Learning |
| Project(s) | No Simula project |
| Publication Type | Poster |
| Year of Publication | 2020 |
| Publisher | ESHRE |
| Place Published | ESHRE virtual 36th Annual Meeting |
Journal Article
A new quantile tracking algorithm using a generalized exponentially weighted average of observations
Applied Intelligence 49 (2019): 1406-1420.Status: Published
A new quantile tracking algorithm using a generalized exponentially weighted average of observations
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Journal | Applied Intelligence |
| Volume | 49 |
| Number | 4 |
| Pagination | 1406–1420 |
| Publisher | {Springer |
Artificial intelligence as a tool in predicting sperm motility and morphology: P-116
Human Reproduction 34 (2019).Status: Published
Artificial intelligence as a tool in predicting sperm motility and morphology: P-116
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Journal | Human Reproduction |
| Volume | 34 |
| Publisher | Oxford Academic |
Artificial intelligence predicts sperm motility from sperm fatty acids: P-120
Human Reproduction 34 (2019).Status: Published
Artificial intelligence predicts sperm motility from sperm fatty acids: P-120
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Journal | Human Reproduction |
| Volume | 34 |
| Publisher | Oxford Academic |
Associations Between Immigration-Related User Factors and eHealth Activities for Self-Care: Case of First-Generation Immigrants From Pakistan in the Oslo Area, Norway
JMIR public health and surveillance 5 (2019): e11998.Status: Published
Associations Between Immigration-Related User Factors and eHealth Activities for Self-Care: Case of First-Generation Immigrants From Pakistan in the Oslo Area, Norway
| Afilliation | Communication Systems |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Journal | JMIR public health and surveillance |
| Volume | 5 |
| Number | 3 |
| Pagination | e11998 |
| Publisher | {JMIR Publications Inc., Toronto, Canada |
Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction
Nature Scientific Reports 9, no. 1 (2019).Status: Published
Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction
Methods for automatic analysis of clinical data are usually targeted towards a specific modality and do not make use of all relevant data available. In the field of male human reproduction, clinical and biological data
are not used to its fullest potential. Manual evaluation of a semen sample using a microscope is time-consuming and requires extensive training. Furthermore, the validity of manual semen analysis has been questioned due to limited reproducibility, and often high inter-personnel variation. The existing computer-aided sperm analyzer systems are not recommended for routine clinical use due to methodological challenges caused by the consistency of the semen sample. Thus, there is a need for an improved methodology.
We use modern and classical machine learning techniques together with a dataset consisting of 85 videos of human semen samples and related participant data to automatically predict sperm motility. Used techniques include simple linear regression and more sophisticated methods using convolutional neural networks. Our results indicate that sperm motility prediction based on deep learning using sperm motility videos is rapid to perform and consistent. The algorithms performed worse when participant data was added. In conclusion, machine learning-based automatic analysis may become a valuable tool in male infertility investigation and research.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Journal | Nature Scientific Reports |
| Volume | 9 |
| Issue | 1 |
| Number | 16770 |
| Publisher | Springer Nature |
On solving the SPL problem using the concept of probability flux
Applied Intelligence 49 (2019): 2699-2722.Status: Published
On solving the SPL problem using the concept of probability flux
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Journal | Applied Intelligence |
| Volume | 49 |
| Number | 7 |
| Pagination | 2699–2722 |
| Publisher | {Springer |
Smooth estimates of multiple quantiles in dynamically varying data streams
Pattern Analysis and Applications (2019): 1-12.Status: Published
Smooth estimates of multiple quantiles in dynamically varying data streams
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Journal | Pattern Analysis and Applications |
| Pagination | 1–12 |
| Publisher | {Springer |
Two-time scale learning automata: an efficient decision making mechanism for stochastic nonlinear resource allocation
Applied Intelligence (2019): 1-14.Status: Published
Two-time scale learning automata: an efficient decision making mechanism for stochastic nonlinear resource allocation
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Journal | Applied Intelligence |
| Pagination | 1–14 |
| Publisher | {Springer |
Proceedings, refereed
Extracting temporal features into a spatial domain using autoencoders for sperm video analysis
In MediaEval 2019. CEUR Workshop Proceedings, 2019.Status: Published
Extracting temporal features into a spatial domain using autoencoders for sperm video analysis
In this paper, we present a two-step deep learning method that is used to predict sperm motility and morphology based on video recordings of human spermatozoa. First, we use an autoencoder to extract temporal features from a given semen video and plot these into image-space, which we call feature-images. Second, these feature-images are used to perform transfer learning to predict the motility and morphology values of human sperm. The presented method shows it's capability to extract temporal information into spatial domain feature-images which can be used with traditional convolutional neural networks. Furthermore, the accuracy of the predicted motility of a given semen sample shows that a deep learning-based model can capture the temporal information of microscopic recordings of human semen.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | MediaEval 2019 |
| Date Published | 10/2019 |
| Publisher | CEUR Workshop Proceedings |
GANEx: A complete pipeline of training, inference and benchmarking GAN experiments
In 2019 International Conference on Content-Based Multimedia Indexing (CBMI). IEEE, 2019.Status: Published
GANEx: A complete pipeline of training, inference and benchmarking GAN experiments
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | 2019 International Conference on Content-Based Multimedia Indexing (CBMI) |
| Pagination | 1-4 |
| Publisher | IEEE |
| Keywords | GAN, GANEx, Generative Adversarial Network |
Medico Multimedia Task at MediaEval 2019
In MediaEval. CEUR Workshop Proceedings, 2019.Status: Published
Medico Multimedia Task at MediaEval 2019
The Medico: Multimedia for Medicine Task is running for the third time as part of MediaEval 2019. This year, we have changed the task from anomaly detection in images of the gastrointestinal tract to focus on the automatic prediction of human semen quality based on videos. The purpose of this task is to aid in the assessment of male reproductive health by providing a quick and consisted method of analyzing human semen. In this paper, we describe the task in detail, give a brief description of the provided dataset, and discuss the evaluation process and the metrics used to rank the submissions of the participants.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | MediaEval |
| Publisher | CEUR Workshop Proceedings |
Predicting Sperm Motility and Morphology using Deep Learning and Handcrafted Features
In MediaEval. ceur ws org, 2019.Status: Published
Predicting Sperm Motility and Morphology using Deep Learning and Handcrafted Features
This paper presents the approach proposed by the organizer team (SimulaMet) for MediaEval 2019 Multimedia for Medicine: The Medico Task. The approach uses a data preparation method which is based on global features extracted from multiple frames within each video and then combines this with information about the patient in order to create a compressed representation of each video. The goal is to create a less hardware expensive data representation that still retains the temporal information of the video and related patient data. Overall, the results need some improvement before being a viable option for clinical use.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | MediaEval |
| Publisher | ceur ws org |
Semantic Analysis of Soccer News for Automatic Game Event Classification
In 2019 International Conference on Content-Based Multimedia Indexing (CBMI). IEEE, 2019.Status: Published
Semantic Analysis of Soccer News for Automatic Game Event Classification
We are today overwhelmed with information, of which an important part is news. Sports news, in particular, has become very popular, where soccer makes up a big part of this coverage. For sports fans, it can be a time consuming and tedious to keep up with the news that they really care about. In this paper, we present different machine learning methods applied to soccer news from a Norwegian newspaper and a TV station's news site to summarize the content in a short and digestible manner. We present a system to collect, index, label, analyze, and present the collected news articles based on the content. We perform a thorough comparison between deep learning and traditional machine learning algorithms on text classification. Furthermore, we present a dataset of soccer news which was collected from two different Norwegian news sites and shared online.
| Afilliation | Machine Learning |
| Project(s) | Simula Metropolitan Center for Digital Engineering, Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | 2019 International Conference on Content-Based Multimedia Indexing (CBMI) |
| Publisher | IEEE |
Stacked dense optical flows and dropout layers to predict sperm motility and morphology
In MediaEval 2019, 27-29 October 2019, Sophia Antipolis, France, 2019.Status: Published
Stacked dense optical flows and dropout layers to predict sperm motility and morphology
In this paper, we analyse two deep learning methods to predict sperm motility and sperm morphology from sperm videos. We use two different inputs: stacked pure frames of videos and dense optical flows of video frames. To solve this regression task of predicting motility and morphology, stacked dense optical flows and extracted original frames from sperm videos were used with the modified state of the art convolution neural networks. For modifications of the selected models, we have introduced an additional multi-layer perceptron to overcome the problem of over-fitting. The method which had an additional multi-layer perceptron with dropout layers, shows the best results when the inputs consist of both dense optical flows and an original frame of videos.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | MediaEval 2019, 27-29 October 2019, Sophia Antipolis, France |
| Date Published | 10/2019 |
THREAT: A Large Annotated Corpus for Detection of Violent Threats
In 2019 International Conference on Content-Based Multimedia Indexing (CBMI). IEEE, 2019.Status: Published
THREAT: A Large Annotated Corpus for Detection of Violent Threats
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | 2019 International Conference on Content-Based Multimedia Indexing (CBMI) |
| Pagination | 1–5 |
| Publisher | IEEE |
VISEM: a multimodal video dataset of human spermatozoa
In Proceedings of the 10th ACM Multimedia Systems Conference. ACM, 2019.Status: Published
VISEM: a multimodal video dataset of human spermatozoa
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | Proceedings of the 10th ACM Multimedia Systems Conference |
| Pagination | 261–266 |
| Publisher | ACM |
Proceedings, refereed
The Medico-Task 2018: Disease Detection in the Gastrointestinal Tract using Global Features and Deep Learning
In MediaEval 2018. Nice, France: MediaEval, 2018.Status: Published
The Medico-Task 2018: Disease Detection in the Gastrointestinal Tract using Global Features and Deep Learning
In this paper, we present our approach for the 2018 Medico Task classifying diseases in the gastrointestinal tract. We have proposed a system based on global features and deep neural networks. The best approach combines two neural networks and the reproducible experimental results signify the efficiency of the proposed model with an accuracy rate of 95.80%, a precision of 95.87%, and an F1-score of 95.80%.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2018 |
| Conference Name | MediaEval 2018 |
| Date Published | 10/2018 |
| Publisher | MediaEval |
| Place Published | Nice, France |
| Keywords | CNN, deep learning, Gastrointestinal Disease Detection, Global Features, Medico-Task 2018, Transfer Learning |