Publications
Proceedings, refereed
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 |
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 |
