Towards non-data-hungry and fully-automated diagnosis of breast cancer from mammographic images
Abstract
Analysing local texture and generating features are two key issues for automatic cancer detection in mammographic images. Recent researches have shown that deep neural networks provide a promising alternative to hand-driven features which suffer from curse of dimensionality and low accuracy rates. However, large and balanced training data are foremost requirements for deep learning-based models and these data are not always available publicly....
Paper Details
Title
Towards non-data-hungry and fully-automated diagnosis of breast cancer from mammographic images
Published Date
Dec 1, 2021
Volume
139
Pages
105011 - 105011
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