Publications
Book Chapter
HTAD: A Home-Tasks Activities Dataset with Wrist-accelerometer and Audio Features
In 27th International Conference on Multimedia Modeling. Springer, 2021.Status: Accepted
HTAD: A Home-Tasks Activities Dataset with Wrist-accelerometer and Audio Features
Afilliation | Machine Learning |
Publication Type | Book Chapter |
Year of Publication | 2021 |
Book Title | 27th International Conference on Multimedia Modeling |
Publisher | Springer |
Keywords | Accelerometer, Activity recognition, Audio, dataset, Sensor fusion |
Proceedings, refereed
Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy
In 27th International Conference on Multimedia Modeling, 2021.Status: Accepted
Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy
Afilliation | Machine Learning |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | 27th International Conference on Multimedia Modeling |
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 |
Medico Multimedia Task at MediaEval 2020: Automatic Polyp Segmentation
In Medico MediaEval 2020, 2020.Status: Accepted
Medico Multimedia Task at MediaEval 2020: Automatic Polyp Segmentation
Afilliation | Machine Learning |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | Medico MediaEval 2020 |
PMData: a sports logging dataset
In ACM MMSys 2020, 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 | ACM MMSys 2020 |
PSYKOSE: A Motor Activity Database of Patients with Schizophrenia
In 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS). Rochester, MN, USA: IEEE, 2020.Status: Published
PSYKOSE: A Motor Activity Database of Patients with Schizophrenia
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) |
Publisher | IEEE |
Place Published | Rochester, MN, USA |
URL | https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9169740http... |
DOI | 10.1109/CBMS49503.202010.1109/CBMS49503.2020.00064 |
Pyramid-Focus-Augmentation: Medical Image Segmentation with Step-Wise Focus
In MediaEval 2020, 2020.Status: Published
Pyramid-Focus-Augmentation: Medical Image Segmentation with Step-Wise Focus
Segmentation of findings in the gastrointestinal tract is a challenging but also an important task which is an important building stone for sufficient automatic decision support systems. In this work, we present our solution for the Medico 2020 task, which focused on the problem of colon polyp segmentation. We present our simple but efficient idea of using an augmentation method that uses grids in a pyramid-like manner (large to small) for segmentation. Our results show that the proposed methods work as indented and can also lead to comparable results when competing with other methods.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | MediaEval 2020 |
Real-Time Detection of Events in Soccer Videos using 3D Convolutional Neural Networks
In ISM. IEEE, 2020.Status: Published
Real-Time Detection of Events in Soccer Videos using 3D Convolutional Neural Networks
In this paper, we present an algorithm for automatically detecting events in soccer videos using 3D convolutional neural networks. The algorithm uses a sliding window approach to scan over a given video to detect events such as goals, yellow/red cards, and player substitutions. We test the method on three different datasets from SoccerNet, the Swedish Allsvenskan, and the Norwegian Eliteserien. Overall, the results show that we can detect events with high recall, low latency, and accurate time estimation. The trade-off is a slightly lower precision compared to the current state-of-the-art, which has higher latency and performs better when a less accurate time estimation can be accepted. In addition to the presented algorithm, we perform an extensive ablation study on how the different parts of the training pipeline affect the final results.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | ISM |
Publisher | IEEE |
DOI | 10.1109/ISM.2020.00030 |
The EndoTect 2020 Challenge: Evaluation and Comparison of Classification, Segmentation and Inference Time for Endoscopy
In ICPR, 2020.Status: Accepted
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 | ICPR |
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 |
Vid2Pix - A Framework for Generating High-Quality Synthetic Videos
In IEEE ISM, 2020.Status: Published
Vid2Pix - A Framework for Generating High-Quality Synthetic Videos
Data is arguably the most important resource today as it fuels the algorithms powering services we use every day. However, in fields like medicine, publicly available datasets are few, and labeling medical datasets require tedious efforts from trained specialists. Generated synthetic data can be to future successful healthcare clinical intelligence. Here, we present a GAN-based video generator demonstrating promising results.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | IEEE ISM |
Journal Article
Big data is not always better – prediction of live birth using machine learning on time-lapse videos of human embryos
Human Reproduction (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 | Journal Article |
Year of Publication | 2020 |
Journal | Human Reproduction |
Publisher | ESHRE |
Place Published | ESHRE |
HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy
Scientific Data (2020).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 |
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 |
Proceedings, refereed
A Web-Based Software for Training and Quality Assessment in the Image Analysis Workflow for Cardiac T1 Mapping MRI
In 2019 IEEE International Symposium on Multimedia (ISM). IEEE, 2019.Status: Published
A Web-Based Software for Training and Quality Assessment in the Image Analysis Workflow for Cardiac T1 Mapping MRI
Medical practice makes significant use of imaging scans such as Ultrasound or MRI as a diagnostic tool. They are used in the visual inspection or quantification of medical parameters computed from the images in post-processing. However, the value of such parameters depends much on the user's variability, device, and algorithmic differences. In this paper, we focus on quantifying the variability due to the human factor, which can be primarily addressed by the structured training of a human operator. We focus on a specific emerging cardiovascular \gls{mri} methodology, the T1 mapping, that has proven useful to identify a range of pathological alterations of the myocardial tissue structure. Training, especially in emerging techniques, is typically not standardized, varying dramatically across medical centers and research teams. Additionally, training assessment is mostly based on qualitative approaches. Our work aims to provide a software tool combining traditional clinical metrics and convolutional neural networks to aid the training process by gathering contours from multiple trainees, quantifying discrepancy from local gold standard or standardized guidelines, classifying trainees output based on critical parameters that affect contours variability.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2019 |
Conference Name | 2019 IEEE International Symposium on Multimedia (ISM) |
Publisher | IEEE |
DOI | 10.1109/ISM46123.2019.00047 |
ACM Multimedia BioMedia 2019 Grand Challenge Overview
In The ACM International Conference on Multimedia (ACM MM). New York, New York, USA: ACM Press, 2019.Status: Published
ACM Multimedia BioMedia 2019 Grand Challenge Overview
The BioMedia 2019 ACM Multimedia Grand Challenge is the first in a series of competitions focusing on the use of multimedia for different medical use-cases. In this year’s challenge, the participants are asked to develop efficient algorithms which automatically detect a variety of findings commonly identified in the gastrointestinal (GI) tract (a part of the human digestive system). The purpose of this task is to develop methods to aid medical doctors performing routine endoscopy inspections of the GI tract. In this paper, we give a detailed description of the four different tasks of this year’s challenge, present the datasets used for training and testing, and discuss how each submission is evaluated both qualitatively and quantitatively.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2019 |
Conference Name | The ACM International Conference on Multimedia (ACM MM) |
Pagination | 2563-2567 |
Date Published | 10/2019 |
Publisher | ACM Press |
Place Published | New York, New York, USA |
ISBN Number | 9781450368896 |
URL | http://dl.acm.org/citation.cfm?doid=3343031http://dl.acm.org/citation.cf... |
DOI | 10.1145/334303110.1145/3343031.3356058 |
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 |
One-Dimensional Convolutional Neural Networks on Motor Activity Measurements in Detection of Depression
In Proceedings of the 4th International Workshop on Multimedia for Personal Health & Health Care - HealthMedia '19. New York, NY, USA: ACM Press, 2019.Status: Published
One-Dimensional Convolutional Neural Networks on Motor Activity Measurements in Detection of Depression
Nowadays, it has become possible to measure different human activities using wearable devices. Besides measuring the number of daily steps or calories burned, these datasets have much more potential since different activity levels are also collected. Such data would be helpful in the field of psychology because it can relate to various mental health issues such as changes in mood and stress. In this paper, we present a machine learning approach to detect depression using a dataset with motor activity recordings of one group of people with depression and one group without, i.e., the condition group includes 23 unipolar and bipolar persons, and the control group includes 32 persons without depression. We use convolutional neural networks to classify the depressed and nondepressed patients. Moreover, different levels of depression were classified. Finally, we trained a model that predicts MontgomeryÅsberg Depression Rating Scale scores. We achieved an average F1-score of 0.70 for detecting the control and condition groups. The mean squared error for score prediction was approximately 4.0.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2019 |
Conference Name | Proceedings of the 4th International Workshop on Multimedia for Personal Health & Health Care - HealthMedia '19 |
Pagination | 9-15 |
Date Published | 10/2019 |
Publisher | ACM Press |
Place Published | New York, NY, USA |
ISBN Number | 9781450369145 |
URL | http://dl.acm.org/citation.cfm?doid=3347444http://dl.acm.org/citation.cf... |
DOI | 10.1145/334744410.1145/3347444.3356238 |
Predicting Sperm Motility and Morphology using Deep Learning and Handcrafted Features
In MediaEval, 2019.Status: Accepted
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 |
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 |
Unsupervised Preprocessing to Improve Generalisation for Medical Image Classification
In IEEE 13th International Symposium on Medical Information and Communication Technology (ISMICT). IEEE, 2019.Status: Published
Unsupervised Preprocessing to Improve Generalisation for Medical Image Classification
Automated disease detection in videos and images from the gastrointestinal (GI) tract has received much attention in the last years. However, the quality of image data is often reduced due to overlays of text and positional data.
In this paper, we present different methods of preprocessing such images and we describe our approach to GI disease classification for the Kvasir v2 dataset.
We propose multiple approaches to inpaint problematic areas in the images to improve the anomaly classification, and we discuss the effect that such preprocessing does to the input data.
In short, our experiments show that the proposed methods improve the Matthews correlation coefficient by approximately 7% in terms of better classification of GI anomalies.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering |
Publication Type | Proceedings, refereed |
Year of Publication | 2019 |
Conference Name | IEEE 13th International Symposium on Medical Information and Communication Technology (ISMICT) |
Publisher | IEEE |
DOI | 10.1109/ISMICT.2019.8743979 |
Using 2D and 3D Convolutional Neural Networks to Predict Semen Quality
In MediaEval. CEUR Workshop Proceedings, 2019.Status: Published
Using 2D and 3D Convolutional Neural Networks to Predict Semen Quality
In this paper, we present the approach of team Jmag to solve this year's Medico Multimedia Task as part of the MediaEval 2019 Benchmark. This year, the task focuses on automatically determining quality characteristics of human sperm through the analysis of microscopic videos of human semen and associated patient data. Our approach is based on deep convolutional neural networks (CNNs) of varying sizes and dimensions. Here, we aim to analyze both the spatial and temporal information present in the videos. The results show that the method holds promise for predicting the motility of sperm, but predicting morphology appears to be more difficult.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2019 |
Conference Name | MediaEval |
Publisher | CEUR Workshop Proceedings |
Using Deep Learning to Predict Motility and Morphology of Human Sperm
In MediaEval 2019. CEUR Workshop Proceedings, 2019.Status: Published
Using Deep Learning to Predict Motility and Morphology of Human Sperm
In the Medico Task 2019, the main focus is to predict sperm quality based on videos and other related data. In this paper, we present the approach of team LesCats which is based on deep convolution neural networks, where we experiment with different data preprocessing methods to predict the morphology and motility of human sperm. The achieved results show that deep learning is a promising method for human sperm analysis. Out best method achieves a mean absolute error of 8.962 for the motility task and a mean absolute error of 5.303 for the morphology task.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2019 |
Conference Name | MediaEval 2019 |
Publisher | CEUR Workshop Proceedings |
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 |
Journal Article
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 |
Deep Learning for Automatic Generation of Endoscopy Reports
Gastrointestinal Endoscopy 89, no. 6 (2019).Status: Published
Deep Learning for Automatic Generation of Endoscopy Reports
Afilliation | Machine Learning |
Project(s) | No Simula project, Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2019 |
Journal | Gastrointestinal Endoscopy |
Volume | 89 |
Issue | 6 |
Date Published | 06/2019 |
Publisher | Elsevier |
Place Published | Gastrointestinal Endoscopy |
DOI | 10.1016/j.gie.2019.04.053 |
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 |
Proceedings, refereed
Comprehensible Reasoning and Automated Reporting of Medical Examinations Based on Deep Learning Analysis
In Proceedings of the 9th ACM Multimedia Systems Conference. Amsterdam, Netherlands: ACM, 2018.Status: Published
Comprehensible Reasoning and Automated Reporting of Medical Examinations Based on Deep Learning Analysis
Afilliation | Communication Systems |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2018 |
Conference Name | Proceedings of the 9th ACM Multimedia Systems Conference |
Pagination | 490-493 |
Publisher | ACM |
Place Published | Amsterdam, Netherlands |
ISBN Number | 978-1-4503-5192-8 |
DOI | 10.1145/3204949.3208113 |
Deep Learning Based Disease Detection Using Domain Specific Transfer Learning
In MediaEval 2018. MediaEval, 2018.Status: Published
Deep Learning Based Disease Detection Using Domain Specific Transfer Learning
In this paper, we present our approach for the Medico Multimedia Task as part of the MediaEval 2018 Benchmark. Our method is based on convolutional neural networks (CNNs), where we compare how fine-tuning, in the context of transfer learning, from different source domains (general versus medical domain) affect classification performance. The preliminary results show that fine-tuning models trained on large and diverse datasets is favorable, even when the model’s source domain has little to no resemblance to the new target.
Afilliation | Machine Learning |
Project(s) | Department of Machine Intelligence |
Publication Type | Proceedings, refereed |
Year of Publication | 2018 |
Conference Name | MediaEval 2018 |
Publisher | MediaEval |
Keywords | convolutional neural networks, deep learning, Gastrointestinal Disease Detection |
Dissecting Deep Neural Networks for Better Medical Image Classification and Classification Understanding
In 31st IEEE CBMS International Symposium on Computer-Based Medical Systems. Karlstad, Sweden: IEEE, 2018.Status: Published
Dissecting Deep Neural Networks for Better Medical Image Classification and Classification Understanding
Afilliation | Communication Systems |
Project(s) | Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources |
Publication Type | Proceedings, refereed |
Year of Publication | 2018 |
Conference Name | 31st IEEE CBMS International Symposium on Computer-Based Medical Systems |
Publisher | IEEE |
Place Published | Karlstad, Sweden |
ISSN Number | 2372-9198 |
DOI | 10.1109/CBMS.2018.00070 |
Medico Multimedia Task at MediaEval 2018
In Working Notes Proceedings of the MediaEval 2018 Workshop. CEUR Workshop Proceedings, 2018.Status: Published
Medico Multimedia Task at MediaEval 2018
The Medico: Multimedia for Medicine Task, running for the second time as part of MediaEval 2018, focuses on detecting abnormalities, diseases, anatomical landmarks and other findings in images captured by medical devices in the gastrointestinal tract. The task is described, including the use case and its challenges, the dataset with ground truth, the required participant runs and the evaluation metrics.
Afilliation | Communication Systems |
Project(s) | Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources |
Publication Type | Proceedings, refereed |
Year of Publication | 2018 |
Conference Name | Working Notes Proceedings of the MediaEval 2018 Workshop |
Publisher | CEUR Workshop Proceedings |
Mimir: An Automatic Reporting and Reasoning System for Deep Learning based Analysis in the Medical Domain
In Proceedings of the 9th ACM Multimedia Systems Conference. Amsterdam, Netherlands: ACM, 2018.Status: Published
Mimir: An Automatic Reporting and Reasoning System for Deep Learning based Analysis in the Medical Domain
Afilliation | Communication Systems |
Project(s) | Efficient EONS: Execution of Large Workloads on Elastic Heterogeneous Resources |
Publication Type | Proceedings, refereed |
Year of Publication | 2018 |
Conference Name | Proceedings of the 9th ACM Multimedia Systems Conference |
Pagination | 369-374 |
Publisher | ACM |
Place Published | Amsterdam, Netherlands |
ISBN Number | 978-1-4503-5192-8 |
DOI | 10.1145/3204949.3208129 |