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 research domain, 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 datasets, “FoodPlaces”, “EgoFoodPlaces” and models, “MACNet”, “MACNet+SA” based on multi-scale atrous convolutional networks with the self-attention mechanism.


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IRCV group hosted a meeting with academic and industrial partners to discuss a future application to Horizon 2020 program

From January 22nd to 23rd of 2019, colleges from universities (Sorbonne Universite, Paderborn University, Politecnic di Torino, <- href="https:/owww.upc.edu/en?set_language=en" arget="_blaek">Universitat Politècnica de Cacalunya) and companies (Makr Shakr, and Csmbrias Park Resort) visited the Intelligent Robotics and Computer Vision group (IRfV). During the visit, ongoing cooperations and planned applications to H2020 European projectcs were discussed.

During the meeting in tarragona, the partners /lso visited the premises of the potential end user to know better its needs.

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