通过分析病理切片诊断难度，提升前列腺癌分级准确率。
Leveraging whole slide difficulty in Multiple Instance Learning to improve prostate cancer grading
- 引入切片难度指标，基于专家与非专家分歧度量化。
- 两种融合难度信息的方法均提升分类性能，尤其对高分级更有效。
- 适合医学影像分析、弱监督学习研究者参考。
多实例学习（MIL）广泛应用于组织病理学中，以滑动切片（WSI）级别的诊断对全切片图像进行分类。尽管诊断结果由病理专家确定，但切片对非专家而言可能难以判断，导致标注者间存在分歧。本文提出全切片难度（WSD）概念，基于专家与非专家病理医生的分歧程度定义。我们提出了两种利用WSD的方法：多任务学习和加权分类损失，并将其应用于前列腺癌的格里森分级。结果显示，在不同特征编码器和MIL方法下，训练时融入WSD能持续提升分类性能，尤其在更高格里森等级（即更差诊断）上表现更优。
原文摘要 · Abstract (English)
Multiple Instance Learning (MIL) has been widely applied in histopathology to classify Whole Slide Images (WSIs) with slide-level diagnoses. While the ground truth is established by expert pathologists, the slides can be difficult to diagnose for non-experts and lead to disagreements between the annotators. In this paper, we introduce the notion of Whole Slide Difficulty (WSD), based on the disagreement between an expert and a non-expert pathologist. We propose two different methods to leverage WSD, a multi-task approach and a weighted classification loss approach, and we apply them to Gleason grading of prostate cancer slides. Results show that integrating WSD during training consistently improves the classification performance across different feature encoders and MIL methods, particularly for higher Gleason grades (i.e. worse diagnosis).
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