Mitesh Mittal, TU Kaiserslautern
Title: Deep learning approaches for medical image data segmentation and
Deep neural networks are now the state-of-the-art machine learning
models across a variety of areas, from computer vision, recommendation
systems, natural language processing, and are also widely deployed in
academia and industry. Their potential for use in medical imaging
technology, medical data analysis, medical diagnostics and healthcare
in general, are slowly being realized. We will look at recent advances
and some associated challenges in deep learning applied to medical
image segmentation and image classification.
The image classification problems arise in diabetic retinopathy
diagnosis. Methods based on UCI experiments often fail on real diabetic
retinopathy data. Bayesian deep learning enables the estimation of
model uncertainty and sets benchmarks based on the uncertainty in
classification. The uncertainty level determines whether an expert
referral is necessary for better diagnosis. The BDL method outperforms
current UCI experiments which overfit their uncertainty to the dataset
and provide better diagnosis diabetic retinopathy diagnosis.
In medical image segmentation, we look at deep Convolutional Neural
Networks (CNN) that use data augmentation on available annotated
samples to give better performance. This model can be trained end-to-
end from very few images and the architecture takes into account the
context and localization of the image patches.
This approach outperforms the prior best method (a sliding-window
convolutional network) on the ISBI challenge for segmentation of
neuronal structures in electron microscopic stacks by large margins.
The striking performance of these methods motivates their application
to other complex problems in medical image data processing.
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