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).

翻译：多实例学习（MIL）已广泛应用于组织病理学中，用于对具有切片级诊断的全切片图像（WSI）进行分类。虽然金标准由病理学专家确定，但这些切片对于非专家而言可能难以诊断，并导致标注者之间的分歧。本文引入了一种基于专家与非专家病理学家之间分歧的全切片难度（WSD）概念。我们提出了两种利用WSD的不同方法：一种多任务方法和一种加权分类损失方法，并将其应用于前列腺癌切片的Gleason分级。结果表明，在训练过程中整合WSD能够持续提升不同特征编码器和MIL方法的分类性能，特别是对于较高的Gleason分级（即更差的诊断）。