Publications
Journal Article
Enhanced Equivalence Projective Simulation: A Framework for Modeling Formation of Stimulus Equivalence Classes
Neural Computation 33 (2021): 1-45.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 |
| Number | 1 |
| Pagination | 1–45 |
| Publisher | {MIT Press |
Proceedings, refereed
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 |
| Publisher | Springer |
| Keywords | Accelerometer, Activity recognition, Audio, dataset, Sensor fusion |
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
| 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 |