Abstract IA-03: Unsupervised resolution of intra- and inter-tumoral heterogeneity using deep learning

Applications of deep learning to histopathology has demonstrated expert-level performance, but approaches have been largely concentrated on supervised classification tasks requiring context-specific training and deployment. We demonstrate that deep neural networks, previously trained to recognize diverse histomorphologies in one environment can effectively extrapolate their learned representations to resolve clinically relevant intra- and inter-patient tissue pattern differences in other cancer types without explicit instruction or additional optimization. Moreover, this unsupervised approach highlighted numerous human misclassifications, suggesting that repositioning of existing histology-educated networks could provide scalable approaches for quality assurance and discovery of unappreciated subgroups of disease. Citation Format: Phedias Diamandis. Unsupervised resolution of intra- and inter-tumoral heterogeneity using deep learning [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-03.