# Benchmarking foundation models as feature extractors for weakly-supervised computational pathology

arXiv: https://arxiv.org/abs/2408.15823 · HTML: https://mlsanity.com/paper/2408.15823/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2024.

## Built on by (most cited first, 30 of 49)

- [Multimodal Whole Slide Foundation Model for Pathology](https://mlsanity.com/paper/2411.19666/) (2024)
- [PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology](https://mlsanity.com/paper/2505.20202/) (2025)
- [Unsupervised Foundation Model-Agnostic Slide-Level Representation Learning](https://mlsanity.com/paper/2411.13623/) (2024)
- [Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts](https://mlsanity.com/paper/2410.06723/) (2024)
- [Scanner-Induced Domain Shifts Undermine the Robustness of Pathology Foundation Models](https://mlsanity.com/paper/2601.04163/) (2026)
- [How Good is my Histopathology Vision-Language Foundation Model? A Holistic Benchmark](https://mlsanity.com/paper/2503.12990/) (2025)
- [Benchmarking ECG FMs: A Reality Check Across Clinical Tasks](https://mlsanity.com/paper/2509.25095/) (2025)
- [SciVid: Cross-Domain Evaluation of Video Models in Scientific Applications](https://mlsanity.com/paper/2507.03578/) (2025)
- [Pathryoshka: Compressing Pathology Foundation Models via Multi-Teacher Knowledge Distillation with Nested Embeddings](https://mlsanity.com/paper/2511.23204/) (2025)
- [TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning](https://mlsanity.com/paper/2512.21331/) (2025)
- [Toward Clinically Explainable AI for Medical Diagnosis: A Foundation Model with Human-Compatible Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2509.03906/) (2025)
- [Fusion of Multi-scale Heterogeneous Pathology Foundation Models for Whole Slide Image Analysis](https://mlsanity.com/paper/2510.27237/) (2025)
- [Adapting a Pre-trained Single-Cell Foundation Model to Spatial Gene Expression Generation from Histology Images](https://mlsanity.com/paper/2603.19766/) (2026)
- [LaB-RAG: Label Boosted Retrieval Augmented Generation for Radiology Report Generation](https://mlsanity.com/paper/2411.16523/) (2024)
- [Benchmarking Foundation Models for Mitotic Figure Classification](https://mlsanity.com/paper/2508.04441/) (2025)
- [GrapHist: Graph Self-Supervised Learning for Histopathology](https://mlsanity.com/paper/2603.00143/) (2026)
- [Unifying Multiple Foundation Models for Advanced Computational Pathology](https://mlsanity.com/paper/2503.00736/) (2025)
- [PhenoBench: A Comprehensive Benchmark for Cell Phenotyping](https://mlsanity.com/paper/2507.03532/) (2025)
- [A Hierarchical Benchmark of Foundation Models for Dermatology](https://mlsanity.com/paper/2601.12382/) (2026)
- [A Comprehensive Benchmark of Histopathology Foundation Models for Kidney Digital Pathology Images](https://mlsanity.com/paper/2603.15967/) (2026)
- [CytoSyn: a Foundation Diffusion Model for Histopathology -- Tech Report](https://mlsanity.com/paper/2603.18089/) (2026)
- [Plug-and-Play Logit Fusion for Heterogeneous Pathology Foundation Models](https://mlsanity.com/paper/2604.07779/) (2026)
- [Benchmarking Pathology Foundation Models for Breast Cancer Survival Prediction](https://mlsanity.com/paper/2604.24679/) (2026)
- [Benchmarking Pathology Foundation Models for Spatial Domain Understanding](https://mlsanity.com/paper/2605.25764/) (2026)
- [Evaluation and Prognostic Validation of Deep Regression Models for WSI-Based Gene-Expression Prediction](https://mlsanity.com/paper/2410.00945/) (2024)
- [Comparing Computational Pathology Foundation Models using Representational Similarity Analysis](https://mlsanity.com/paper/2509.15482/) (2025)
- [Information-driven Fusion of Pathology Foundation Models for Enhanced Disease Characterization](https://mlsanity.com/paper/2512.11104/) (2025)
- [Explainable histomorphology-based survival prediction of glioblastoma, IDH-wildtype](https://mlsanity.com/paper/2601.11691/) (2026)
- [Benchmarking Computational Pathology Foundation Models For Semantic Segmentation](https://mlsanity.com/paper/2602.18747/) (2026)
- [BRIGHT: A Collaborative Generalist-Specialist Foundation Model for Breast Pathology](https://mlsanity.com/paper/2603.03030/) (2026)

## Abstract (arXiv)

Advancements in artificial intelligence have driven the development of numerous pathology foundation models capable of extracting clinically relevant information. However, there is currently limited literature independently evaluating these foundation models on truly external cohorts and clinically-relevant tasks to uncover adjustments for future improvements. In this study, we benchmarked 19 histopathology foundation models on 13 patient cohorts with 6,818 patients and 9,528 slides from lung, colorectal, gastric, and breast cancers. The models were evaluated on weakly-supervised tasks related to biomarkers, morphological properties, and prognostic outcomes. We show that a vision-language foundation model, CONCH, yielded the highest performance when compared to vision-only foundation models, with Virchow2 as close second. The experiments reveal that foundation models trained on distinct cohorts learn complementary features to predict the same label, and can be fused to outperform the current state of the art. An ensemble combining CONCH and Virchow2 predictions outperformed individual models in 55% of tasks, leveraging their complementary strengths in classification scenarios. Moreover, our findings suggest that data diversity outweighs data volume for foundation models. Our work highlights actionable adjustments to improve pathology foundation models.