  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.
