Publications
Proceedings, refereed
Extracting temporal features into a spatial domain using autoencoders for sperm video analysis
In MediaEval 2019, 27-29 October 2019, Sophia Antipolis, France, 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, 27-29 October 2019, Sophia Antipolis, France |
| Date Published | 10/2019 |
GANEx: A complete pipeline of training, inference and benchmarking GAN experiments
In CBMI - 2019, at Dublin City University, Dublin, Ireland, 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 | CBMI - 2019, at Dublin City University, Dublin, Ireland |
| 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). https://ieeexplore.ieee.org/document/8743979: 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 |
| Place Published | https://ieeexplore.ieee.org/document/8743979 |
Journal Article
Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction
Nature Scientific Reports (2019).Status: Accepted
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 |
| Publisher | Nature Publishing Group |
Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction
Nature Scientific Reports (2019).Status: Accepted
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 |
| 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 |
