Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charité, and Aignostics
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913643536318464 |
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| author | Alber, Maximilian Tietz, Stephan Dippel, Jonas Milbich, Timo Lesort, Timothée Korfiatis, Panos Krügener, Moritz Cancer, Beatriz Perez Shah, Neelay Möllers, Alexander Seegerer, Philipp Carpen-Amarie, Alexandra Standvoss, Kai Dernbach, Gabriel de Jong, Edwin Schallenberg, Simon Kunft, Andreas von Ankershoffen, Helmut Hoffer Schaeferle, Gavin Duffy, Patrick Redlon, Matt Jurmeister, Philipp Horst, David Ruff, Lukas Müller, Klaus-Robert Klauschen, Frederick Norgan, Andrew |
| author_facet | Alber, Maximilian Tietz, Stephan Dippel, Jonas Milbich, Timo Lesort, Timothée Korfiatis, Panos Krügener, Moritz Cancer, Beatriz Perez Shah, Neelay Möllers, Alexander Seegerer, Philipp Carpen-Amarie, Alexandra Standvoss, Kai Dernbach, Gabriel de Jong, Edwin Schallenberg, Simon Kunft, Andreas von Ankershoffen, Helmut Hoffer Schaeferle, Gavin Duffy, Patrick Redlon, Matt Jurmeister, Philipp Horst, David Ruff, Lukas Müller, Klaus-Robert Klauschen, Frederick Norgan, Andrew |
| contents | 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 Charité - Universtätsmedizin 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_05409 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charité, and Aignostics Alber, Maximilian Tietz, Stephan Dippel, Jonas Milbich, Timo Lesort, Timothée Korfiatis, Panos Krügener, Moritz Cancer, Beatriz Perez Shah, Neelay Möllers, Alexander Seegerer, Philipp Carpen-Amarie, Alexandra Standvoss, Kai Dernbach, Gabriel de Jong, Edwin Schallenberg, Simon Kunft, Andreas von Ankershoffen, Helmut Hoffer Schaeferle, Gavin Duffy, Patrick Redlon, Matt Jurmeister, Philipp Horst, David Ruff, Lukas Müller, Klaus-Robert Klauschen, Frederick Norgan, Andrew Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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 Charité - Universtätsmedizin 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. |
| title | Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charité, and Aignostics |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2501.05409 |