mlsanityData to 7 Oct 2026

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

arXiv 2501.05409 ↗First posted 9 Jan 2025cs.CV cs.AI cs.LGcited by 10

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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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 18 of its 40 references that are in the map. First posted on arXiv 9 Jan 2025.

Builds on

18 of 40 references.

References · 18

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,764
  3. 2018Attention-based Deep Multiple Instance Learningcited by 1,077
  4. 2018Rotation Equivariant CNNs for Digital Pathologycited by 420
  5. 2024Multimodal Whole Slide Foundation Model for Pathologycited by 156
  6. 2022Benchmarking Self-Supervised Learning on Diverse Pathology Datasetscited by 148
  7. 2018BACH: Grand Challenge on Breast Cancer Histology Imagescited by 131
  8. 2024HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysiscited by 104
  9. 2024PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathologycited by 80
  10. 2024Phikon-v2, A large and public feature extractor for biomarker predictioncited by 67
  11. 2021A Petri Dish for Histopathology Image Analysiscited by 63
  12. 2024Hibou: A Family of Foundational Vision Transformers for Pathologycited by 52
6 more
  1. 2024RudolfV: A Foundation Model by Pathologists for Pathologistscited by 40
  2. 2024PLUTO: Pathology-Universal Transformercited by 17
  3. 2024Unsupervised Foundation Model-Agnostic Slide-Level Representation Learningcited by 15
  4. 2024AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnosticscited by 8
  5. 2022Evaluating histopathology transfer learning with ChampKitcited by 7
  6. 2024From Pixels to Gigapixels: Bridging Local Inductive Bias and Long-Range Dependencies with Pixel-Mambacited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 7; 2026: 3.

10 papers.

  1. 2025Distilling foundation models for robust and efficient models in digital pathologycited by 33
  2. 2025Training state-of-the-art pathology foundation models with orders of magnitude less datacited by 19
  3. 2025Foundation Models in Medical Imaging: A Review and Outlookcited by 7
  4. 2025MeDi: Metadata-Guided Diffusion Models for Mitigating Biases in Tumor Classificationcited by 6
  5. 2025Benchmarking Foundation Models for Mitotic Figure Classificationcited by 2
  6. 2025PLUTO-4: Frontier Pathology Foundation Modelscited by 2
  7. 2026OpenTME: An Open Dataset of AI-powered H&E Tumor Microenvironment Profiles from TCGAcited by 2
  8. 2025Generalisation of automatic tumour segmentation in histopathological whole-slide images across multiple cancer typescited by 0
  9. 2026Atlas H&E-TME: Scalable AI-Based Tissue Profiling at Expert Pathologist-Level Accuracycited by 0
  10. 2026Morphology signal in whole slide image foundation models can automatically triage slidescited by 0