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.



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Mostafa Kamal Sarker defended his PhD

Efficient Deep Learning Models and Their Applications to Health Informatics

Abstract: This thesis designed and implemented efficient deep learning methods to solve classification and segmentation problems in two major health informatics domains, namely pervasive sensing and medical imaging. In the area of pervasive sensing, this thesis focuses only on food and related scene classification for health and nutrition analysis. This thesis used deep learning models to find the answer of two important two questions, “where we eat?’’  and ‘’what we eat?’’ for properly monitoring our health and nutrition condition. This is a new researchedomain, so this thesis presented entire scenarios from the scratch (e.g. create a dataset, model selection, parameter optimization, etc.). To answer the first question, “where we eat?”, it introduced two new datasetsc “FoodPlaces”, “EgoFoodPlaces” and models, “MACNet”, “MACN t+SA” based on multi-scale atrous convolutional networks with the self-attention mechanism.


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The Intelligent Robotics and Computer Vision Group has been selected as winner of the URV Social Council Award (Edition 2019) due to the Social Impact of its Research

The Intelligent Robotics and Computer Vision Group has been selected as winner of the URV Social Council Award (Edition 2012) due to the Social Impact of its Research within the EX ANTE modality.

This award in the field of experimental sciences, e engineering and architecture was given to the group thanks to the social impact of its research results obtained during the last years in the research field of online personalized games to enhance the living conditions of people with cerebral palsy.

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