Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities

• URL: http://arxiv.org/abs/2111.02400v1
• Date: Mon, 1 Nov 2021 15:31:32 GMT
• Title: Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities
• Authors: Tianbao Yang
• Abstract summary: We will present and discuss opportunities and challenges brought by a new deep learning method by AUC (aka underlinebf Deep underlinebf AUC classification)
• Score: 60.079782224958414
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this extended abstract, we will present and discuss opportunities and challenges brought about by a new deep learning method by AUC maximization (aka \underline{\bf D}eep \underline{\bf A}UC \underline{\bf M}aximization or {\bf DAM}) for medical image classification. Since AUC (aka area under ROC curve) is a standard performance measure for medical image classification, hence directly optimizing AUC could achieve a better performance for learning a deep neural network than minimizing a traditional loss function (e.g., cross-entropy loss). Recently, there emerges a trend of using deep AUC maximization for large-scale medical image classification. In this paper, we will discuss these recent results by highlighting (i) the advancements brought by stochastic non-convex optimization algorithms for DAM; (ii) the promising results on various medical image classification problems. Then, we will discuss challenges and opportunities of DAM for medical image classification from three perspectives, feature learning, large-scale optimization, and learning trustworthy AI models.

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