PathBot: A Foundation Model for Pathological Image Analysis

Computational pathology has emerged as a transformative paradigm by leveraging artificial intelligence to automate and enhance diagnostic procedures. However, existing models often target narrow tasks or specific tumor types, missing opportunities to unify diverse datasets and tasks through joint learning. In this work, we introduce PathBot, a foundation model tailored for comprehensive pathological image analysis. Central to PathBot is a ViT-Giant encoder with one billion parameters, the largest model to date trained on publicly available pathological data. We pre-train this encoder using a novel Masked Distillation Network (MDN) and an integrated learning strategy that combines contrastive and generative objectives. The pre-training leverages over 30 million image patches derived from 11,765 whole slide images (WSIs) across 32 cancer types in the Cancer Genome Atlas (TCGA). To evaluate its versatility, we pair the encoder with task-specific decoders for segmentation, detection, classification, and regression. Extensive experiments across 20 downstream tasks demonstrate that PathBot achieves state-of-the-art performance in most cases, showcasing its robustness and generalizability.