Deep Learning Based Analysis of Cancer Similarities and Subtyping Using Histopathological Whole Slide Images

Piyush Singh

Abstract

Whole slide images (WSIs) contain rich information for computational pathology, yet systematic evaluations of cross-organ and multi-cohort generalization remain limited. This thesis addresses these challenges through two complementary studies. In the first study, patch-level convolutional neural networks (CNNs) were trained on 9,792 slides from The Cancer Genome Atlas (TCGA), spanning 11 cancer subtypes across seven organs, to distinguish cancerous from normal tissue. Both within-organ and cross-organ inference were evaluated, revealing that cancers such as breast, colorectal, and liver can be reliably detected by models trained on other organs. Strong transferability was also observed between subtypes within an organ, such as kidney and lung. To investigate these patterns, feature similarity, overlap in high-attention regions, and nuclear geometry were analyzed, all of which showed positive correlations with cross-organ transfer performance. The second study focuses on lung cancer in the Indian population through the introduction of IPD-Lung, a curated dataset of adenocarcinoma and squamous carcinoma cases. Benchmark evaluations were established using multiple instance learning (MIL) models, with additional experiments exploring model transferability from publicly available TCGA-Lung dataset. Stain normalization methods, particularly Macenko, reduced domain discrepancies to some extent but did not fully bridge performance gaps, especially for squamous carcinoma. A domain adaptation strategy based on a gradient reversal layer (GRL) similarly yielded limited improvements. In contrast, models trained directly on IPD-Lung achieved substantial performance gains, with further enhancements obtained by training on expert-annotated regions of interest (RoIs). Together, these studies demonstrate that deep learning models can uncover meaningful cross-organ similarities in cancer histopathology. 

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