Publications
Journal Article
Meta-learning with implicit gradients in a few-shot setting for medical image segmentation
Computers in Biology and Medicine 14312136320119704317507593739403621582 (2022).Status: Published
Meta-learning with implicit gradients in a few-shot setting for medical image segmentation
Widely used traditional supervised deep learning methods require a large number of training samples but often fail to generalize on unseen datasets. Therefore, a more general application of any trained model is quite limited for medical imaging for clinical practice. Using separately trained models for each unique lesion category or a unique patient population will require sufficiently large curated datasets, which is not practical to use in a real-world clinical set-up. Few-shot learning approaches can not only minimize the need for an enormous number of reliable ground truth labels that are labour-intensive and expensive, but can also be used to model on a dataset coming from a new population. To this end, we propose to exploit an optimization-based implicit model agnostic meta-learning (iMAML) algorithm under few-shot settings for medical image segmentation. Our approach can leverage the learned weights from diverse but small training samples to perform analysis on unseen datasets with high accuracy. We show that, unlike classical few-shot learning approaches, our method improves generalization capability. To our knowledge, this is the first work that exploits iMAML for medical image segmentation and explores the strength of the model on scenarios such as meta-training on unique and mixed instances of lesion datasets. Our quantitative results on publicly available skin and polyp datasets show that the proposed method outperforms the naive supervised baseline model and two recent few-shot segmentation approaches by large margins. In addition, our iMAML approach shows an improvement of 2%–4% in dice score compared to its counterpart MAML for most experiments.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | Computers in Biology and Medicine |
Volume | 14312136320119704317507593739403621582 |
Date Published | Jan-04-2022 |
Publisher | Elsevier |
ISSN | 00104825 |
URL | https://linkinghub.elsevier.com/retrieve/pii/S0010482522000191 |
DOI | 10.1016/j.compbiomed.2022.105227 |
On evaluation metrics for medical applications of artificial intelligenceAbstract
Nature Scientific Reports 1236825221520218325484051210158697682437, no. 1 (2022).Status: Published
On evaluation metrics for medical applications of artificial intelligenceAbstract
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | Nature Scientific Reports |
Volume | 1236825221520218325484051210158697682437 |
Issue | 1 |
Date Published | Jan-12-2022 |
Publisher | Nature |
URL | https://www.nature.com/articles/s41598-022-09954-8https://www.nature.com... |
DOI | 10.1038/s41598-022-09954-8 |
Miscellaneous
MMSys'22 Grand Challenge on AI-based Video Production for Soccer
arXiv, 2022.Status: Published
MMSys'22 Grand Challenge on AI-based Video Production for Soccer
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Miscellaneous |
Year of Publication | 2022 |
Publisher | arXiv |
Other Numbers | arXiv:2202.01031 |
URL | https://arxiv.org/abs/2202.01031 |
Journal Article
A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging
Medical Image Analysis 70 (2021): 102007.Status: Published
A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging
Gastrointestinal endoscopy has been an active field of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. The high missed rate of such abnormalities during endoscopy is thus a critical bottleneck. Lack of attentiveness due to tiring procedures, and requirement of training are few contributing factors. An automatic GI disease classification system can help reduce such risks by flagging suspicious frames and lesions. GI endoscopy consists of several multi-organ surveillance, therefore, there is need to develop methods that can generalize to various endoscopic findings. In this realm, we present a comprehensive analysis of the Medico GI challenges: Medical Multimedia Task at MediaEval 2017, Medico Multimedia Task at MediaEval 2018, and BioMedia ACM MM Grand Challenge 2019. These challenges are initiative to set-up a benchmark for different computer vision methods applied to the multi-class endoscopic images and promote to build new approaches that could reliably be used in clinics. We report the performance of 21 participating teams over a period of three consecutive years and provide a detailed analysis of the methods used by the participants, highlighting the challenges and shortcomings of the current approaches and dissect their credibility for the use in clinical settings. Our analysis revealed that the participants achieved an improvement on maximum Mathew correlation coefficient (MCC) from 82.68% in 2017 to 93.98% in 2018 and 95.20% in 2019 challenges, and a significant increase in computational speed over consecutive years.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | Medical Image Analysis |
Volume | 70 |
Pagination | 102007 |
Publisher | Elsevier |
Keywords | Artificial intelligence, BioMedia 2019 Grand Challenge, Computer-aided detection and diagnosis, Gastrointestinal endoscopy challenges, Medical imaging, Medico Task 2017, Medico Task 2018 |
DOI | 10.1016/j.media.2021.102007 |
AI-Based Video Clipping of Soccer Events
Machine Learning and Knowledge Extraction 3, no. 4 (2021): 990-1008.Status: Published
AI-Based Video Clipping of Soccer Events
The current gold standard for extracting highlight clips from soccer games is the use of manual annotations and clippings, where human operators define the start and end of an event and trim away the unwanted scenes. This is a tedious, time-consuming, and expensive task, to the extent of being rendered infeasible for use in lower league games. In this paper, we aim to automate the process of highlight generation using logo transition detection, scene boundary detection, and optional scene removal. We experiment with various approaches, using different neural network architectures on different datasets, and present two models that automatically find the appropriate time interval for extracting goal events. These models are evaluated both quantitatively and qualitatively, and the results show that we can detect logo and scene transitions with high accuracy and generate highlight clips that are highly acceptable for viewers. We conclude that there is considerable potential in automating the overall soccer video clipping process.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | Machine Learning and Knowledge Extraction |
Volume | 3 |
Issue | 4 |
Pagination | 990 - 1008 |
Date Published | 12/2021 |
Publisher | MDPI |
URL | https://www.mdpi.com/2504-4990/3/4/49/pdf |
DOI | 10.3390/make3040049 |
Automated Event Detection and Classification in Soccer: The Potential of Using Multiple Modalities
Machine Learning and Knowledge Extraction 3, no. 4 (2021): 1030-1054.Status: Published
Automated Event Detection and Classification in Soccer: The Potential of Using Multiple Modalities
Detecting events in videos is a complex task, and many different approaches, aimed at a large variety of use-cases, have been proposed in the literature. Most approaches, however, are unimodal and only consider the visual information in the videos. This paper presents and evaluates different approaches based on neural networks where we combine visual features with audio features to detect (spot) and classify events in soccer videos. We employ model fusion to combine different modalities such as video and audio, and test these combinations against different state-of-the-art models on the SoccerNet dataset. The results show that a multimodal approach is beneficial. We also analyze how the tolerance for delays in classification and spotting time, and the tolerance for prediction accuracy, influence the results. Our experiments show that using multiple modalities improves event detection performance for certain types of events.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | Machine Learning and Knowledge Extraction |
Volume | 3 |
Issue | 4 |
Pagination | 1030 - 1054 |
Date Published | 12/2021 |
Publisher | MDPI |
URL | https://www.mdpi.com/2504-4990/3/4/51/pdf |
DOI | 10.3390/make3040051 |
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 |
Explaining deep neural networks for knowledge discovery in electrocardiogram analysis
Scientific Reports 11 (2021): 10949.Status: Published
Explaining deep neural networks for knowledge discovery in electrocardiogram analysis
Deep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction should be accompanied by an explanation that a human can understand. We present an approach called electrocardiogram gradient class activation map (ECGradCAM), which is used to generate attention maps and explain the reasoning behind deep learning-based decision-making in ECG analysis. Attention maps may be used in the clinic to aid diagnosis, discover new medical knowledge, and identify novel features and characteristics of medical tests. In this paper, we showcase how ECGradCAM attention maps can unmask how a novel deep learning model measures both amplitudes and intervals in 12-lead electrocardiograms, and we show an example of how attention maps may be used to develop novel ECG features.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | Scientific Reports |
Volume | 11 |
Pagination | 10949 |
Date Published | 05/2021 |
Publisher | Springer Nature |
URL | http://www.nature.com/articles/s41598-021-90285-5h |
DOI | 10.1038/s41598-021-90285-5 |
Impact of Image Resolution on Deep Learning Performance in Endoscopy Image Classification: An Experimental Study Using a Large Dataset of Endoscopic Images
Diagnostics 11, no. 12 (2021).Status: Published
Impact of Image Resolution on Deep Learning Performance in Endoscopy Image Classification: An Experimental Study Using a Large Dataset of Endoscopic Images
Recent trials have evaluated the efficacy of deep convolutional neural network (CNN)-based AI systems to improve lesion detection and characterization in endoscopy. Impressive results are achieved, but many medical studies use a very small image resolution to save computing resources at the cost of losing details. Today, no conventions between resolution and performance exist, and monitoring the performance of various CNN architectures as a function of image resolution provides insights into how subtleties of different lesions on endoscopy affect performance. This can help set standards for image or video characteristics for future CNN-based models in gastrointestinal (GI) endoscopy. This study examines the performance of CNNs on the HyperKvasir dataset, consisting of 10,662 images from 23 different findings. We evaluate two CNN models for endoscopic image classification under quality distortions with image resolutions ranging from 32 × 32 to 512 × 512 pixels. The performance is evaluated using two-fold cross-validation and F1-score, maximum Matthews correlation coefficient (MCC), precision, and sensitivity as metrics. Increased performance was observed with higher image resolution for all findings in the dataset. MCC was achieved at image resolutions between 512 × 512 pixels for classification for the entire dataset after including all subclasses. The highest performance was observed with an MCC value of 0.9002 when the models were trained on the highest resolution and tested on the same resolution. Different resolutions and their effect on CNNs are explored. We show that image resolution has a clear influence on the performance which calls for standards in the field in the future.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | Diagnostics |
Volume | 11 |
Issue | 12 |
Date Published | 09/2021 |
Publisher | MDPI |
URL | https://www.mdpi.com/2075-4418/11/12/2183 |
DOI | 10.3390/diagnostics11122183 |
Kvasir-Capsule, a video capsule endoscopy dataset
Scientific Data 8, no. 1 (2021): 142.Status: Published
Kvasir-Capsule, a video capsule endoscopy dataset
Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital. Kvasir-Capsule consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The Kvasir-Capsule dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | Scientific Data |
Volume | 8 |
Issue | 1 |
Pagination | 142 |
Publisher | Springer Nature |
URL | http://www.nature.com/articles/s41597-021-00920-z |
DOI | 10.1038/s41597-021-00920-z |
Using 3D Convolutional Neural Networks for Real-time Detection of Soccer Events
International Journal of Semantic Computing 15, no. 2 (2021): 161-187.Status: Published
Using 3D Convolutional Neural Networks for Real-time Detection of Soccer Events
Developing systems for the automatic detection of events in video is a task which has gained attention in many areas including sports. More specifically, event detection for soccer videos has been studied widely in the literature. However, there are still a number of shortcomings in the state-of-the-art such as high latency, making it challenging to operate at the live edge. In this paper, we present an algorithm to detect events in soccer videos in real time, using 3D convolutional neural networks. We test our algorithm 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 | Communication Systems, Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | International Journal of Semantic Computing |
Volume | 15 |
Issue | 2 |
Number | 2 |
Pagination | 161 - 187 |
Date Published | Jan-06-2021 |
Publisher | World Scientific |
ISSN | 1793-351X |
Keywords | 3d CNN, classification, Detection, soccer events, spotting |
URL | https://www.worldscientific.com/doi/abs/10.1142/S1793351X2140002X |
DOI | 10.1142/S1793351X2140002X |
Book Chapter
Artificial Intelligence in Gastroenterology
In Artificial Intelligence in Medicine, 1-20. Cham: Springer International Publishing, 2021.Status: Published
Artificial Intelligence in Gastroenterology
The holy grail in endoscopy examinations has for a long time been assisted diagnosis using Artificial Intelligence (AI). Recent developments in computer hardware are now enabling technology to equip clinicians with promising tools for computer-assisted diagnosis (CAD) systems. However, creating viable models or architectures, training them, and assessing their ability to diagnose at a human level, are complicated tasks. This is currently an active area of research, and many promising methods have been proposed. In this chapter, we give an overview of the topic. This includes a description of current medical challenges followed by a description of the most commonly used methods in the field. We also present example results from research targeting some of these challenges, and a discussion on open issues and ongoing work is provided. Hopefully, this will inspire and enable readers to future develop CAD systems for gastroenterology.
Afilliation | Communication Systems, Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Book Chapter |
Year of Publication | 2021 |
Book Title | Artificial Intelligence in Medicine |
Pagination | 1 - 20 |
Date Published | 09/2021 |
Publisher | Springer International Publishing |
Place Published | Cham |
ISBN Number | 978-3-030-58080-3 |
Keywords | Anomaly detection, artificial intelligence, Gastrointestinal endoscopy, Hand-crafted features, Neural Networks, Performance, Semantic segmentation |
URL | https://link.springer.com/referenceworkentry/10.1007%2F978-3-030-58080-3... |
DOI | 10.1007/978-3-030-58080-3_163-2 |
Proceedings, refereed
Automated Clipping of Soccer Events using Machine Learning
In IEEE International Symphosium of Multimedia (ISM). IEEE, 2021.Status: Published
Automated Clipping of Soccer Events using Machine Learning
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | IEEE International Symphosium of Multimedia (ISM) |
Date Published | 12/2021 |
Publisher | IEEE |
DOI | 10.1109/ISM52913.2021.00042 |
Data Augmentation Using Generative Adversarial Networks For Creating Realistic Artificial Colon Polyp Images
In DDW 2021, 2021.Status: Published
Data Augmentation Using Generative Adversarial Networks For Creating Realistic Artificial Colon Polyp Images
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | DDW 2021 |
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 |
DivergentNets: Medical Image Segmentation by Network Ensemble
In EndoCV, 2021.Status: Accepted
DivergentNets: Medical Image Segmentation by Network Ensemble
Detection of colon polyps has become a trending topic in the intersecting fields of machine learning and gastrointestinal endoscopy. The focus has mainly been on per-frame classification. More recently, polyp segmentation has gained attention in the medical community. Segmentation has the advantage of being more accurate than per-frame classification or object detection as it can show the affected area in greater detail. For our contribution to the EndoCV 2021 segmentation challenge, we propose two separate approaches. First, a segmentation model named TriUNet composed of three separate UNet models. Second, we combine TriUNet with an ensemble of well-known segmentation models, namely UNet++, FPN, DeepLabv3, and DeepLabv3+, into a model called DivergentNets to produce more generalizable medical image segmentation masks. In addition, we propose a modified Dice loss that calculates loss only for a single class when performing multi-class segmentation, forcing the model to focus on what is most important. Overall, the proposed methods achieved the best average scores for each respective round in the challenge, with TriUNet being the winning model in Round I and DvergentNets being the winning model in Round II of the segmentation generalization challenge at EndoCV 2021
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | EndoCV |
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 |
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 |
Impact of Image Resolution on Convolutional Neural Networks Performance in Gastrointestinal Endoscopy
In DDW 2021, 2021.Status: Published
Impact of Image Resolution on Convolutional Neural Networks Performance in Gastrointestinal Endoscopy
Convolutional neural networks (CNNs) are increasingly used to improve and automate processes in gastroenterology, like the detection of polyps during a colonoscopy. An important input to these methods is images and videos. Up until now, no well-defined, common understanding or standard regarding the resolution of the images and video frames has been defined, and to reduce processing time and resource requirements, images are today almost always down-sampled. However, how such down-sampling and the image resolution influence the performance in context with medical data is unknown. In this work, we investigate how the resolution relates to the performance of convolutional neural networks. This can help set standards for image or video characteristics for future CNN based models in gastrointestinal endoscopy.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | DDW 2021 |
Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy
In 27th International Conference on Multimedia Modeling. Vol. LNCS, volume 12573. Springer, 2021.Status: Published
Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy
Gastrointestinal (GI) pathologies are periodically screened, biopsied, and resected using surgical tools. Usually the procedures and the treated or resected areas are not specifically tracked or analysed during or after colonoscopies. Information regarding disease borders, development and amount and size of the resected area get lost. This can lead to poor follow-up and bothersome reassessment difficulties post-treatment. To improve the current standard and also to foster more research on the topic we have released the ``Kvasir Instrument'' dataset which consists of $590$ annotated frames containing GI procedure tools such as snares, balloons and biopsy forceps, etc. Beside of the images, the dataset includes ground truth masks and bounding boxes and has been verified by two expert GI endoscopists. Additionally, we provide a baseline for the segmentation of the GI tools to promote research and algorithm development. We obtained a dice coefficient score of 0.9158 and a Jaccard index of 0.8578 using a classical U-Net architecture. A similar dice coefficient score was observed for DoubleUNet. The qualitative results showed that the model did not work for the images with specularity and the frames with multiple instruments, while the best result for both methods was observed on all other types of images. Both, qualitative and quantitative results show that the model performs reasonably good, but there is a large potential for further improvements. Benchmarking using the dataset provides an opportunity for researchers to contribute to the field of automatic endoscopic diagnostic and therapeutic tool segmentation for GI endoscopy.
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 |
Volume | LNCS, volume 12573 |
Pagination | 218-229 |
Publisher | Springer |
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/ |
Multimodal Virtual Avatars for Investigative Interviews with Children
In Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval (ICDAR '21). New York, NY, USA: ACM, 2021.Status: Published
Multimodal Virtual Avatars for Investigative Interviews with Children
In this article, we present our ongoing work in the field of training police officers who conduct interviews with abused children. The objectives in this context are to protect vulnerable children from abuse, facilitate prosecution of offenders, and ensure that innocent adults are not accused of criminal acts. There is therefore a need for more data that can be used for improved interviewer training to equip police with the skills to conduct high-quality interviews. To support this important task, we propose to research a training program that utilizes different system components and multimodal data from the field of artificial intelligence such as chatbots, generation of visual content, text-to-speech, and speech-to-text. This program will be able to generate an almost unlimited amount of interview and also training data. The goal of combining all these different technologies and datatypes is to create an immersive and interactive child avatar that responds in a realistic way, to help to support the training of police interviewers, but can also produce synthetic data of interview situations that can be used to solve different problems in the same domain.
Afilliation | Communication Systems, 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 (ICDAR '21) |
Publisher | ACM |
Place Published | New York, NY, USA |
ISBN Number | 9781450385299 |
URL | https://dl.acm.org/doi/proceedings/10.1145/3463944 |
DOI | 10.1145/346394410.1145/3463944.3469269 |
PhD Thesis
DeepSynthBody: the beginning of the end for data deficiency in medicine
In Oslo Metropolitan University. Vol. PhD, 2021.Status: Published
DeepSynthBody: the beginning of the end for data deficiency in medicine
Recent advancements in technology have made artificial intelligence (AI) a popular tool in the medical domain, especially machine learning (ML) methods, which is a subset of AI. In this context, a goal is to research and develop generalizable and well-performing ML models to be used as the main component in computer-aided diagnosis (CAD) systems. However, collecting and processing medical data has been identified as a major obstacle to produce AI-based solutions in the medical domain. In addition to the focus on the development of ML models, this thesis also aims at finding a solution to the data deficiency problem caused by, for example, privacy concerns and the tedious medical data annotation process.
To accomplish the goals of the thesis, we investigated case studies from three different medical branches, namely cardiology, gastroenterology, and andrology. Using data from these case studies, we developed ML models. Addressing the scarcity of medical data, we collected, analyzed, and developed medical datasets and performed benchmark analyses. A framework for generating synthetic medical data has been developed using generative adversarial networks (GANs) as a solution to address the data deficiency problem. Our results indicate that our generated synthetic data may be a solution to the data challenge. As an overarching concept, we introduced the DeepSynthBody as a basis for structured and centralized synthetic medical data generation. The studies presented in the thesis, such as generating synthetic electrocardiograms (ECGs), gastrointestinal (GI)-tract images and videos with and without polyps, and sperm samples, showed that DeepSynthBody can help to overcome data privacy concerns, the time-consuming and costly data annotation process, and the data imbalance problem in the medical domain. Our experiments showed that our generative models generate realistic synthetic data providing comparable results to experiments using real data to tackle the identified problems. The final DeepSynthBody framework is available as an open-source project that allows researchers, industry, and practitioners to use the system and contribute to future developments.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | PhD Thesis |
Year of Publication | 2021 |
Degree awarding institution | Oslo Metropolitan University |
Degree | PhD |
Number of Pages | 387 |
Date Published | 12/2021 |
Thesis Type | Article-based thesis |
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 |
Kvasir-instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy
In International Conference on Multimedia Modeling. Springer, 2020.Status: Published
Kvasir-instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy
Gastrointestinal pathologies are periodically screened, biopsied, and resected using surgical tools. Usually, the procedures and the treated or resected areas are not specifically tracked or analysed during or after colonoscopies. Information regarding disease borders, development, amount, and size of the resected area get lost. This can lead to poor follow-up and bothersome reassessment difficulties post-treatment. To improve the current standard and also to foster more research on the topic, we have released the ``Kvasir-Instrument'' dataset, which consists of 590 annotated frames containing GI procedure tools such as snares, balloons, and biopsy forceps, etc. Besides the images, the dataset includes ground truth masks and bounding boxes and has been verified by two expert GI endoscopists. Additionally, we provide a baseline for the segmentation of the GI tools to promote research and algorithm development. We obtained a dice coefficient score of 0.9158 and a Jaccard index of 0.8578 using a classical U-Net architecture. A similar dice coefficient score was observed for DoubleUNet. The qualitative results showed that the model did not work for the images with specularity and the frames with multiple tools, while the best result for both methods was observed on all other types of images. Both qualitative and quantitative results show that the model performs reasonably good, but there is potential for further improvements. Benchmarking using the dataset provides an opportunity for researchers to contribute to the field of automatic endoscopic diagnostic and therapeutic tool segmentation for GI endoscopy.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | International Conference on Multimedia Modeling |
Publisher | Springer |
Keywords | Benchmarking, Convolutional neural network, Gastrointestinal endoscopy, Tool segmentation |
URL | https://www.springerprofessional.de/en/kvasir-instrument-diagnostic-and-... |
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 |
PSYKOSE: A Motor Activity Database of Patients with Schizophrenia
In 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) |
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. CEUR, 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 |
Publisher | CEUR |
Real-Time Detection of Events in Soccer Videos using 3D Convolutional Neural Networks
In 2020 IEEE International Symposium on Multimedia (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 | 2020 IEEE International Symposium on Multimedia (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 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 |
Vid2Pix - A Framework for Generating High-Quality Synthetic Videos
In 2020 IEEE International Symposium on Multimedia (ISM). IEEE, 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 | 2020 IEEE International Symposium on Multimedia (ISM) |
Publisher | IEEE |
Journal Article
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 |
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 |
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 |
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 |
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 |
Journal Article
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
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 |
Using preprocessing as a tool in medical image detection
In MediaEval 2018. Nice, France: MediaEval, 2018.Status: Published
Using preprocessing as a tool in medical image detection
In this paper, we describe our approach to gastrointestinal disease classification for the medico task at MediaEval 2018. We propose multiple ways to inpaint problematic areas in the test and training set to help with classification. We discuss the effect that preprocessing does to the input data with respect to removing regions with sparse information. We also discuss how preprocessing affects the training and evaluation of a dataset that is limited in size. We will also compare the different inpainting methods with transfer learning using a convolutional neural network.
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 | classification, Image processing, Machine Leanring |