# Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charit'e, and Aignostics

arXiv: https://arxiv.org/abs/2501.05409 · HTML: https://mlsanity.com/paper/2501.05409/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 40 references that are in the map.
- First posted on arXiv 9 Jan 2025.

## 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)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Rotation Equivariant CNNs for Digital Pathology](https://mlsanity.com/paper/1806.03962/) (2018)
- [Multimodal Whole Slide Foundation Model for Pathology](https://mlsanity.com/paper/2411.19666/) (2024)
- [Benchmarking Self-Supervised Learning on Diverse Pathology Datasets](https://mlsanity.com/paper/2212.04690/) (2022)
- [BACH: Grand Challenge on Breast Cancer Histology Images](https://mlsanity.com/paper/1808.04277/) (2018)
- [HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis](https://mlsanity.com/paper/2406.16192/) (2024)
- [PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology](https://mlsanity.com/paper/2405.10254/) (2024)
- [Phikon-v2, A large and public feature extractor for biomarker prediction](https://mlsanity.com/paper/2409.09173/) (2024)
- [A Petri Dish for Histopathology Image Analysis](https://mlsanity.com/paper/2101.12355/) (2021)
- [Hibou: A Family of Foundational Vision Transformers for Pathology](https://mlsanity.com/paper/2406.05074/) (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)
- [Unsupervised Foundation Model-Agnostic Slide-Level Representation Learning](https://mlsanity.com/paper/2411.13623/) (2024)
- [AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics](https://mlsanity.com/paper/2406.14866/) (2024)
- [Evaluating histopathology transfer learning with ChampKit](https://mlsanity.com/paper/2206.06862/) (2022)
- [From Pixels to Gigapixels: Bridging Local Inductive Bias and Long-Range Dependencies with Pixel-Mamba](https://mlsanity.com/paper/2412.16711/) (2024)

## Built on by (most cited first, 11 of 11)

- [Distilling foundation models for robust and efficient models in digital pathology](https://mlsanity.com/paper/2501.16239/) (2025)
- [Training state-of-the-art pathology foundation models with orders of magnitude less data](https://mlsanity.com/paper/2504.05186/) (2025)
- [Foundation Models in Medical Imaging: A Review and Outlook](https://mlsanity.com/paper/2506.09095/) (2025)
- [MeDi: Metadata-Guided Diffusion Models for Mitigating Biases in Tumor Classification](https://mlsanity.com/paper/2506.17140/) (2025)
- [Benchmarking Foundation Models for Mitotic Figure Classification](https://mlsanity.com/paper/2508.04441/) (2025)
- [PLUTO-4: Frontier Pathology Foundation Models](https://mlsanity.com/paper/2511.02826/) (2025)
- [OpenTME: An Open Dataset of AI-powered H&E Tumor Microenvironment Profiles from TCGA](https://mlsanity.com/paper/2604.12075/) (2026)
- [Atlas H&E-TME: Scalable AI-Based Tissue Profiling at Expert Pathologist-Level Accuracy](https://mlsanity.com/paper/2606.12346/) (2026)
- [Generalisation of automatic tumour segmentation in histopathological whole-slide images across multiple cancer types](https://mlsanity.com/paper/2510.11182/) (2025)
- [LUCAID: Agentic Multimodal AI for Lung Cancer Precision Pathology](https://mlsanity.com/paper/2608.23803/) (2026)
- [Morphology signal in whole slide image foundation models can automatically triage slides](https://mlsanity.com/paper/2609.01987/) (2026)

## Abstract (arXiv)

Recent advances in digital pathology have demonstrated the effectiveness of foundation models across diverse applications. In this report, we present Atlas, a novel vision foundation model based on the RudolfV approach. Our model was trained on a dataset comprising 1.2 million histopathology whole slide images, collected from two medical institutions: Mayo Clinic and Charit'e - Universt"atsmedizin Berlin. Comprehensive evaluations show that Atlas achieves state-of-the-art performance across twenty-one public benchmark datasets, even though it is neither the largest model by parameter count nor by training dataset size.