# Multimodal Whole Slide Foundation Model for Pathology

arXiv: https://arxiv.org/abs/2411.19666 · HTML: https://mlsanity.com/paper/2411.19666/

## Key facts

- Cited by 162 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 134 references that are in the map.
- First posted on arXiv 29 Nov 2024.

## Builds on (most cited first, 18 of 18)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [iBOT: Image BERT Pre-Training with Online Tokenizer](https://mlsanity.com/paper/2111.07832/) (2021)
- [PaliGemma: A versatile 3B VLM for transfer](https://mlsanity.com/paper/2407.07726/) (2024)
- [Rotary Position Embedding for Vision Transformer](https://mlsanity.com/paper/2403.13298/) (2024)
- [SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning](https://mlsanity.com/paper/1911.04623/) (2019)
- [Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology](https://mlsanity.com/paper/2408.00738/) (2024)
- [PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology](https://mlsanity.com/paper/2405.10254/) (2024)
- [A Clinical Benchmark of Public Self-Supervised Pathology Foundation Models](https://mlsanity.com/paper/2407.06508/) (2024)
- [A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model](https://mlsanity.com/paper/2407.15362/) (2024)
- [Benchmarking foundation models as feature extractors for weakly-supervised computational pathology](https://mlsanity.com/paper/2408.15823/) (2024)
- [RudolfV: A Foundation Model by Pathologists for Pathologists](https://mlsanity.com/paper/2401.04079/) (2024)
- [PLUTO: Pathology-Universal Transformer](https://mlsanity.com/paper/2405.07905/) (2024)
- [Frozen Feature Augmentation for Few-Shot Image Classification](https://mlsanity.com/paper/2403.10519/) (2024)
- [AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image](https://mlsanity.com/paper/2303.06371/) (2023)

## Built on by (most cited first, 30 of 162)

- [Patho-R1: A Multimodal Reinforcement Learning-Based Pathology Expert Reasoner](https://mlsanity.com/paper/2505.11404/) (2025)
- [SurgVLM: A Large Vision-Language Model and Systematic Evaluation Benchmark for Surgical Intelligence](https://mlsanity.com/paper/2506.02555/) (2025)
- [CPathAgent: An Agent-based Foundation Model for Interpretable High-Resolution Pathology Image Analysis Mimicking Pathologists' Diagnostic Logic](https://mlsanity.com/paper/2505.20510/) (2025)
- [Do Multiple Instance Learning Models Transfer?](https://mlsanity.com/paper/2506.09022/) (2025)
- [Evidence-based diagnostic reasoning with multi-agent copilot for human pathology](https://mlsanity.com/paper/2506.20964/) (2025)
- [A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks](https://mlsanity.com/paper/2501.15724/) (2025)
- [A Survey of Pathology Foundation Model: Progress and Future Directions](https://mlsanity.com/paper/2504.04045/) (2025)
- [Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charit'e, and Aignostics](https://mlsanity.com/paper/2501.05409/) (2025)
- [PixCell: A generative foundation model for digital histopathology images](https://mlsanity.com/paper/2506.05127/) (2025)
- [Multi-Modal Foundation Models for Computational Pathology: A Survey](https://mlsanity.com/paper/2503.09091/) (2025)
- [Scanner-Induced Domain Shifts Undermine the Robustness of Pathology Foundation Models](https://mlsanity.com/paper/2601.04163/) (2026)
- [SurGen: 1020 H&E-stained Whole Slide Images With Survival and Genetic Markers](https://mlsanity.com/paper/2502.04946/) (2025)
- [GECKO: Gigapixel Vision-Concept Contrastive Pretraining in Histopathology](https://mlsanity.com/paper/2504.01009/) (2025)
- [Foundation Models in Medical Imaging: A Review and Outlook](https://mlsanity.com/paper/2506.09095/) (2025)
- [PRISM2: Unlocking Multi-Modal General Pathology AI with Clinical Dialogue](https://mlsanity.com/paper/2506.13063/) (2025)
- [A Large-Scale Benchmark of Cross-Modal Learning for Histology and Gene Expression in Spatial Transcriptomics](https://mlsanity.com/paper/2508.01490/) (2025)
- [MiCo: Multiple Instance Learning with Context-Aware Clustering for Whole Slide Image Analysis](https://mlsanity.com/paper/2506.18028/) (2025)
- [WSI-Agents: A Collaborative Multi-Agent System for Multi-Modal Whole Slide Image Analysis](https://mlsanity.com/paper/2507.14680/) (2025)
- [Navigating Gigapixel Pathology Images with Large Multimodal Models](https://mlsanity.com/paper/2511.19652/) (2025)
- [Atlas 2 -- Foundation models for clinical deployment](https://mlsanity.com/paper/2601.05148/) (2026)
- [PathNavigate: A Training-Free Pathology Agent with Surprise-Guided Scan and Shared Slide Memory for Whole-Slide Image VQA](https://mlsanity.com/paper/2605.23559/) (2026)
- [Pathology Report Generation and Multimodal Representation Learning for Cutaneous Melanocytic Lesions](https://mlsanity.com/paper/2502.19293/) (2025)
- [From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research](https://mlsanity.com/paper/2507.09028/) (2025)
- [Beyond the Failures: Rethinking Foundation Models in Pathology](https://mlsanity.com/paper/2510.23807/) (2025)
- [LoC-Path: Learning to Compress for Pathology Multimodal Large Language Models](https://mlsanity.com/paper/2512.05391/) (2025)
- [TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning](https://mlsanity.com/paper/2512.21331/) (2025)
- [CARE: A Molecular-Guided Foundation Model with Adaptive Region Modeling for Whole Slide Image Analysis](https://mlsanity.com/paper/2602.21637/) (2026)
- [PolyPath: Adapting a Large Multimodal Model for Multi-slide Pathology Report Generation](https://mlsanity.com/paper/2502.10536/) (2025)
- [Benchmarking Foundation Models and Parameter-Efficient Fine-Tuning for Prognosis Prediction in Medical Imaging](https://mlsanity.com/paper/2506.18434/) (2025)
- [Enhancing Zero-Shot Brain Tumor Subtype Classification via Fine-Grained Patch-Text Alignment](https://mlsanity.com/paper/2508.01602/) (2025)

## Abstract (arXiv)

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 disease-specific 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.