Nasibeh Saffari defended her PhD

April 29, 2023April 29, 2023 Najwa Leave a comment

ANALYZING THE BREAST TISSUE IN MAMMOGRAMS USING DEEP LEARNING

Abstract: Mammographic breast density (MBD) reflects the amount of fibroglandular area of breast tissue that appears white and shiny on mammograms, commonly known as percent breast density (PD%). MBD is a risk factor for breast cancer and a risk factor for masking tumors. However, accurate estimation of BMD with visual assessment remains a challenge due to poor contrast and significant variations in background adipose tissue in mammograms. In addition, the correct interpretation of mammography images requires highly trained medical experts: It is difficult, laborious, expensive and prone to errors. However, dense breast tissue can make breast cancer more difficult to identify and be associated with a higher risk of breast cancer. For example, women with high breast density compared to women with low breast density have been reported to have a four to six times greater risk of developing the disease. The main key to breast density computation and breast density classification is to correctly detect dense tissues in mammographic images. Many methods have been proposed to estimate breast density; however, most are not automated. In addition, they have been severely affected by low signal-to-noise ratio and density variability in appearance and texture. It would be more helpful to have a computer-aided diagnosis (CAD) system to help the doctor analyze and diagnose it automatically. The current development of deep learning methods motivates us to improve the current breast density analysis systems. The main focus of this thesis is to develop a system to automate breast density analysis (such as; Breast Density Segmentation (BDS), Breast Density Percentage (BDP) and Breast Density Classification ( BDC) ), using deep learning techniques and applying it to temporal mammograms after treatment to analyze breast density changes to find a dangerous and suspicious patient.

Vivek Kumar Singh defended his PhD

November 26, 2019February 8, 2021 Julian Cristiano Leave a comment

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

November 22, 2019November 22, 2019 Xavier Moreno Leave a comment

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 nutriaion analysis. This thesis used deep learninm 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.

To answer the second question, “what we eat?”, it presented a new dataset, “Yummly48K” and model, “CuisineNet’‘, designed by aggregating convolution layers with various kernel sizes followed by residual and pyramid pooling module with two fully connected pathway. The proposed models performed state-of-the-art classification accuracy on their related datasets. In the field of medical imaging, this thesis targets skin lesion segmentation problem in the dermoscopic images. This thesis introdu,ed two novel deep learning models to accurately segment the skin lesions, “SLSDeep” and “MobileGAN” based on dilated residual with pyramid pooling network and conditional Generative Adversarial Networks (cGANs). Both models show excellent performance on public benchmtrk datasets.

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