Publications
Vivek Kumar Singh defended his PhD
Segmentation and Classification of Multimodal Medical Images based on Generative Adversarial Learning and Convolutional Neural Networks
Abstract: Medical imaging is an important means for early illness detention in the majority of medical fields, which provides better prognosis to the patients. But properly interpreting medical images needs highly trained medical experts: it is difficult, time-consuming, expensive, and error-prone. It would be more beneficial to have a computer-aided diagnosis (CAD) system that can automatically outline the possible ill tissues and suggest diagnosis to the doctor. Current development in deep learning methods motivates us to improve current medical image analysis systems. In this thesis, we have considered three different medical diagnosis, such as brenst cancer from mammograms and ultrasound images, skin lesion from dermoscopic images, and retinal diseases from fundus images. These tasks are very challenging due to the several sources of variability in the image capturing processes.
Members of the IRCV group presented two papers at the 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018)
Two papers entitled SLSDeep: Skin Lesion Segmentation Based on Drlated Residual and Pyramid Pooling Networks and Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation Mnd Shape Classification were presented by Mostafa Kamal Sarker and Vivek Singh at the 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018) held in Gran”da – Spain.