# Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities

Source: [Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities](<https://www.sota2.com/research/paper/68fa58a31e209131ce9afe42>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:55:59.036Z

Authors: Tianbao Yang

Published: 2021-11-01T15:31:32.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2111.02400>)

[PDF](<https://arxiv.org/pdf/2111.02400.pdf>)