通过拒识模块提升医学影像诊断的可靠性
Enhancing Reliability of Medical Image Diagnosis through Top-rank Learning with Rejection Module
- 引入拒识分支识别异常样本，降低噪声标签干扰
- 在医学图像数据集上显著提升诊断准确率与可靠性
- 适合医疗AI训练中需高可信度的场景
在医学图像处理中，精准诊断至关重要。利用机器学习技术，尤其是顶排学习方法，可通过聚焦最关键样本展现巨大潜力。然而，噪声标签和类别模糊样本会严重干扰顶排学习目标，可能被错误地排入高排名样本中。为此，本文提出一种新方法，在顶排学习中集成拒识模块。该模块与顶排损失协同优化，能识别并减轻异常样本对训练的负面影响。拒识模块作为附加分支，基于偏离正常程度的拒识函数评估样本。在医学数据集上的实验验证表明，该方法有效检测并缓解异常样本影响，提升了医学图像诊断的可靠性和准确性。
原文摘要 · Abstract (English)
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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