Mahmood Lab

CONCH (CONtrastive learning from Captions for Histopathology) is a vision-language foundation model from Mahmood Lab (Harvard/BWH). Pretrained on 1.17M histopathology image-text pairs from diverse sources (PubMed, educational resources, textbooks). Evaluated across 14 clinically relevant tasks including zero-shot cancer classification, text-to-image retrieval, image-to-text retrieval, caption generation, and tissue segmentation. Outperforms standard models including CLIP and PLIP. CONCH also works on non-H&E stains (IHC, special stains), demonstrating broad applicability. Available as an open-source model for academic use.

We introduce CONCH, a visual-language foundation model developed using diverse sources of histopathology images and text. Trained on 1.17 million pathology image-text pairs, CONCH achieves state-of-the-art performance across 14 clinically relevant tasks, including zero-shot cancer classification, text-to-image and image-to-text retrieval, caption generation, and tissue segmentation. CONCH outperforms standard models like CLIP and PLIP, and generalizes to non-H&E stains including immunohistochemistry and special stains, demonstrating its versatility as a foundation model for computational pathology.

TITAN (Transformer-based pathology Image and Text Alignment Network) is a multimodal whole-slide foundation model from Mahmood Lab (Harvard/BWH). Pretrained on 335,645 WSIs via visual self-supervised learning and vision-language alignment with 423K synthetic captions from PathChat + 183K pathology reports. Without any fine-tuning, TITAN produces general-purpose slide representations for zero-shot classification, rare cancer retrieval, cross-modal retrieval, and pathology report generation. Outperforms both ROI and slide foundation models across diverse clinical tasks.

The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests into versatile feature representations. However, translating these advancements to address complex clinical challenges at the patient and slide level remains constrained by limited clinical data. We propose TITAN, a multimodal whole-slide foundation model pretrained using 335,645 whole-slide images via visual self-supervised learning and vision-language alignment with pathology reports and 423,122 synthetic captions. Without any fine-tuning, 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.