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

2025/08/11 by Xiaotong Ji, Ji, Xiaotong, Ryoma Bise +3
Computer Science · Medicine · #Anomaly Detection Techniques and Applications #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning and Data Classification

paper · pdf · doi:10.48550/arxiv.2508.07528

openalex publication_date 2025/08/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Dual Conditioned Diffusion Models for Out-Of-Distribution Detection: Application to Fetal Ultrasound Videos
• Out-of-Distribution Detection for Long-tailed and Fine-grained Skin Lesion Images
• Adaptive Weighting Multi-Field-of-View CNN for Semantic Segmentation in Pathology
• SelectiveNet: A Deep Neural Network with an Integrated Reject Option
• Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers
• Deep Neural Networks are Easily Fooled: High Confidence Predictions for\n Unrecognizable Images

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