Multimodal Whole Slide Foundation Model for Pathology

Anurag Jayant Vaidya, Tong Ding, Sophia J. Wagner, Andrew H. Song, Faisal Mahmood

Abstract:

The field of computational pathology has been transformed with recent advances in foundation models
that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL). However, translating these advancements to address complex
clinical challenges at the patient and slide level remains constrained by limited clinical data in diseasespecific cohorts, especially for rare clinical conditions. We propose TITAN, a multimodal whole slide
foundation model pretrained using 335,645 WSIs via visual self-supervised learning and vision-language
alignment with corresponding pathology reports and 423,122 synthetic captions generated from a multimodal generative AI copilot for pathology. Without any finetuning or requiring clinical labels, TITAN
can extract general-purpose slide representations and generate pathology reports that generalize to
resource-limited clinical scenarios such as rare disease retrieval and cancer prognosis. We evaluate
TITAN on diverse clinical tasks and find that TITAN outperforms both ROI and slide foundation models across machine learning settings such as linear probing, few-shot and zero-shot classification, rare
cancer retrieval and cross-modal retrieval, and pathology report generation. The model is publicly accessible at https://github.com/mahmoodlab/TITAN