Long-Tailed Medical Image Classification

Abstract:In this paper, we examine the difficulties of using standard techniques for medical image classification due to long-tailed distributions (wherein rarer conditions have very few samples) resulting in bias towards diagnosing common diseases and away from rarer diseases. We then discuss and implement deep learning models with techniques such as augmentation to minimize error, especially from rarer diseases. We evaluate various different models with AP, F1 score, AUROC, and loss (all on the validation set). We conclude with the promising results from our best model, and potential applications in the healthcare space.

Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

arXiv:2607.23883 [cs.CV]

(or arXiv:2607.23883v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2607.23883 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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