利用整张切片难度在多实例学习中提高前列腺癌分级
Leveraging whole slide difficulty in Multiple Instance Learning to improve prostate cancer grading
AI总结：
本文提出利用整张切片难度提升前列腺癌Gleason分级的分类性能，通过多任务和加权分类损失方法改进诊断效果。
摘要：
多实例学习（MIL）已被广泛应用于组织病理学中，用于对整张切片图像（WSIs）进行分类并进行切片级诊断。虽然地面真实值是由专家病理科医生确定的，但非专家可能难以诊断这些切片，导致标注者之间产生分歧。在本文中，我们引入了整张切片难度（WSD）的概念，基于专家和非专家病理科医生之间的分歧。我们提出了两种不同的方法来利用WSD，即多任务方法和加权分类损失方法，并将它们应用于前列腺癌Gleason分级的切片。结果显示，在不同特征编码器和MIL方法中，整合WSD进行训练始终提高了分类性能，特别是在较高Gleason等级（即更差的诊断）方面。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
