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arXiv · 2508.07528

Enhancing Reliability of Medical Image Diagnosis through Top-rank Learning with Rejection Module

Xiaotong Ji·Ryoma Bise·Seiichi Uchida
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Abstract

In medical image processing, accurate diagnosis is of paramount importance. Leveraging machine learning techniques, particularly top-rank learning, shows significant promise by focusing on the most crucial instances. However, challenges arise from noisy labels and class-ambiguous instances, which can severely hinder the top-rank objective, as they may be erroneously placed among the top-ranked instances. To address these, we propose a novel approach that enhances toprank learning by integrating a rejection module. Cooptimized with the top-rank loss, this module identifies and mitigates the impact of outliers that hinder training effectiveness. The rejection module functions as an additional branch, assessing instances based on a rejection function that measures their deviation from the norm. Through experimental validation on a medical dataset, our methodology demonstrates its efficacy in detecting and mitigating outliers, improving the reliability and accuracy of medical image diagnoses.

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Xiaotong Ji, Ryoma Bise, Seiichi Uchida. 2025-08-11. Enhancing Reliability of Medical Image Diagnosis through Top-rank Learning with Rejection Module. https://arxiv.org/abs/2508.07528

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