Enhancing Reliability of Medical Image Diagnosis through Top-rank Learning with Rejection Module
This work addresses reliability issues in medical image diagnosis for healthcare applications, representing an incremental improvement to existing top-rank learning methods.
The paper tackled the problem of noisy labels and class-ambiguous instances hindering top-rank learning in medical image diagnosis by integrating a rejection module, resulting in improved reliability and accuracy as demonstrated on a medical dataset.
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.