Towards Understanding and Harnessing the Transferability of Prognostic Knowledge in Computational Pathology

📄 Abstract

Abstract: Whole-Slide Image (WSI) is an important tool for estimating cancer prognosis. Current studies generally follow a conventional cancer-specific paradigm where one cancer corresponds to one model. However, it naturally struggles to scale to rare tumors and cannot utilize the knowledge of other cancers. Although a multi-task learning-like framework has been studied recently, it usually has high demands on computational resources and needs considerable costs in iterative training on ultra-large multi-cancer WSI datasets. To this end, this paper makes a paradigm shift to knowledge transfer and presents the first preliminary yet systematic study on cross-cancer prognosis knowledge transfer in WSIs, called CROPKT. It has three major parts: (i) we curate a large dataset (UNI2-h-DSS) with 26 cancers and use it to measure the transferability of WSI-based prognostic knowledge across different cancers (including rare tumors); (ii) beyond a simple evaluation merely for benchmark, we design a range of experiments to gain deeper insights into the underlying mechanism of transferability; (iii) we further show the utility of cross-cancer knowledge transfer, by proposing a routing-based baseline approach (ROUPKT) that could often efficiently utilize the knowledge transferred from off-the-shelf models of other cancers. We hope CROPKT could serve as an inception and lay the foundation for this nascent paradigm, i.e., WSI-based prognosis prediction with cross-cancer knowledge transfer. Our source code is available at https://github.com/liupei101/CROPKT.