PDF(Pubmed)
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.
摘要：
整个幻灯片图像（WSI），通过高分辨率数字扫描显微镜载玻片在多个尺度上获得，是现代数字病理学的基石。然而,它们代表了基于AI/AI介导的分析的特殊挑战，因为病理标记通常在幻灯片级别完成，而不是平铺级。不仅仅是医学诊断记录在样本级别，癌基因突变的检测也是通过实验获得的，并由癌症基因组图谱（TCGA）等计划记录，在幻灯片级别。这构成了双重挑战:a)准确预测总体癌症表型和b)找出在平铺水平上与其相关的细胞形态。为了应对这些挑战，针对两种流行的癌症类型，探索了一种弱监督多实例学习(MIL)方法，浸润性乳腺癌（TCGA-BRCA）和肺鳞癌（TCGA-LUSC）。探索了这种方法用于低放大倍数水平的肿瘤检测和各种水平的TP53突变。我们的结果表明，MIL的新型附加实现与参考实现的性能相匹配(AUC0.96)，并且仅略微优于注意MIL（AUC0.97）。更有趣的是，从分子病理学家的角度来看，这些不同的人工智能架构识别出对形态特征的不同敏感性(通过检测感兴趣的区域，不同扩增水平的RoI)。很明显，TP53突变对细胞形态得以解决的较高应用中的特征最敏感。
