SurvMILKD: A Weakly Supervised Survival Analysis Framework for Multi-Teacher Knowledge Distillation using Pathology Foundation Models

Accurate survival modeling using whole slide images (WSI) is crucial for guiding cancer treatment and improving patient outcomes, yet it remains challenging due to the gigapixel resolution, small data cohorts, and limited annotations. In recent years, pathology foundation models (FM) have shown impressive performance improvements over the previous benchmarks set by the ImageNet-based backbones across a variety of WSI analysis tasks. However, the performance of individual FMs, especially in a secondary analysis task such as survival modeling, varies depending on the dataset, without a clear consensus for a superior FM. With over 25 publicly available pathology FMs so far, in this work, we address the growing challenge of model selection for survival analysis in digital pathology. We propose SurvMILKD, the first multi-teacher knowledge distillation (MKD) framework for WSIbased survival analysis. To efficiently enable MKD with high resolution of the WSIs, we integrate the weakly supervised multiple instance learning (MIL) adapters in our framework. The objective of the framework is to distill the complementary task-specific knowledge from multiple teacher FMs into the student FM, which, during inference, has collective knowledge of all FMs involved during training. Additionally, we introduce the risk-aware distillation loss to distill outcome-specific knowledge from multiple teachers into the student model. Our experiments on eight public datasets (including two external evaluation datasets) demonstrate that the proposed approach outperforms the individual FMs, their ensemble, as well as the prior MKD approaches that we benchmarked on survival analysis for the first time in literature. The codes are available at https://github.com/AIMLab-UBC/SurvMILKD.