Abstract
Whole Slide Images (WSI), obtained by high-resolution digital scanning of microscope slides at multiple scales, are the cornerstone of modern Digital Pathology. However, they represent a particular challenge to AI-based/AI-mediated analysis because pathology labeling is typically done at slide-level, instead of tile-level. It is not just that medical diagnostics is recorded at the specimen level, the detection of oncogene mutation is also experimentally obtained, and recorded by initiatives like The Cancer Genome Atlas (TCGA), at the slide level. This configures a dual challenge: a) accurately predicting the overall cancer phenotype and b) finding out what cellular morphologies are associated with it at the tile level. To address these challenges, a weakly supervised Multiple Instance Learning (MIL) approach was explored for two prevalent cancer types, Invasive Breast Carcinoma (TCGA-BRCA) and Lung Squamous Cell Carcinoma (TCGA-LUSC). This approach was explored for tumor detection at low magnification levels and TP53 mutations at various levels. Our results show that a novel additive implementation of MIL matched the performance of reference implementation (AUC 0.96), and was only slightly outperformed by Attention MIL (AUC 0.97). More interestingly from the perspective of the molecular pathologist, these different AI architectures identify distinct sensitivities to morphological features (through the detection of Regions of Interest, RoI) at different amplification levels. Tellingly, TP53 mutation was most sensitive to features at the higher applications where cellular morphology is resolved.
Abstract (translated)
Whole Slide Images（ WSI）是通过高分辨率数字扫描显微镜幻灯片的多尺度获得的，是现代数字病理学的基石。然而，它们对基于/介导分析来说是一个特别的挑战，因为通常是在幻灯片级别而不是单元级别对病理学进行标注。这不仅是医疗诊断记录在样品级别，抑癌基因突变也在幻灯片级别通过如 The Cancer Genome Atlas（TCGA）等倡议实验获得并记录。这构成了一个双重挑战：a）准确预测整体癌症表型和 b）在单元级别发现与它相关的细胞形态学。为了应对这些挑战，我们探讨了两种常见的癌症类型的弱监督多重实例学习（MIL）方法，侵袭性乳腺癌（TCGA-BRCA）和肺鳞状细胞癌（TCGA-LUSC）。这种方法在低放大倍数水平上探索了肿瘤检测和 TP53 突变 various 级别。我们的结果表明，与参考实现（AUC 0.96）相匹敌的新颖的 MIL 实现了卓越的性能，并略微超过了注意力 MIL（AUC 0.97）。从分子病理学家的角度来看，这些不同的 AI 架构对形态学特征的敏感性有所不同（通过检测感兴趣区域 RoI）。值得注意的是，TP53 突变在较高应用中对于细胞形态学有较清晰的分辨率时最为敏感。
URL
https://arxiv.org/abs/2404.01446
