Feature Extraction
PyTorch
English
moozy
pathology
computational-pathology
digital-pathology
foundation-model
whole-slide-image
vision-transformer
self-supervised-learning
slide-encoder
case-encoder
histopathology
medical-imaging
multiple-instance-learning
slide-level-representation
patient-level-representation
multi-task-learning
survival-analysis
cancer
oncology
tissue-classification
mutation-prediction
TCGA
CPTAC
transformer
Eval Results (legacy)
docs: sync badges with GitHub README, add arXiv TODO markers and full citation
Browse files
README.md
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# MOOZY: A Patient-First Foundation Model for Computational Pathology
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<p align="center">
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<a href="https://atlasanalyticslab.github.io/MOOZY/"><img src="https://img.shields.io/badge/Project-Page-4285F4?logo=googlechrome&logoColor=white" alt="Project Page"></a>
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<a href="https://github.com/AtlasAnalyticsLab/MOOZY"><img src="https://img.shields.io/badge/GitHub-Repository-181717?logo=github" alt="GitHub"></a>
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<a href="https://pypi.org/project/moozy/"><img src="https://img.shields.io/pypi/v/moozy?logo=pypi&logoColor=white&label=PyPI" alt="PyPI"></a>
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<a href="
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</p>
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MOOZY is a slide and patient-level foundation model for computational pathology. The patient case, not the individual slide, is the core unit of representation. A vision-only slide encoder pretrained with masked self-distillation on 77,134 public slides is aligned with clinical semantics through multi-task supervision over 333 tasks (205 classification, 128 survival) from 56 public datasets spanning 23 anatomical sites. A case transformer explicitly models dependencies across all slides from the same patient, replacing the naive early/late fusion used by prior methods. 85.77M total parameters. Trained entirely on public data.
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## Citation
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```bibtex
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@article{
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title
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author
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}
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```
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# MOOZY: A Patient-First Foundation Model for Computational Pathology
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<!-- TODO: update arXiv URL when paper is posted -->
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<p align="center">
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<a href="https://atlasanalyticslab.github.io/MOOZY/"><img src="https://img.shields.io/badge/Project-Page-4285F4?logo=googlechrome&logoColor=white" alt="Project Page"></a>
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<a href="https://arxiv.org/abs/XXXX.XXXXX"><img src="https://img.shields.io/badge/arXiv-XXXX.XXXXX-B31B1B?logo=arxiv" alt="arXiv"></a>
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<a href="https://github.com/AtlasAnalyticsLab/MOOZY"><img src="https://img.shields.io/badge/GitHub-Repository-181717?logo=github" alt="GitHub"></a>
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<!-- TODO: update PyPI badge once first version is published -->
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<a href="https://pypi.org/project/moozy/"><img src="https://img.shields.io/pypi/v/moozy?logo=pypi&logoColor=white&label=PyPI" alt="PyPI"></a>
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<a href="https://github.com/AtlasAnalyticsLab/MOOZY/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey" alt="License"></a>
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<a href="https://www.python.org/"><img src="https://img.shields.io/badge/Python-3.10%2B-blue?logo=python&logoColor=white" alt="Python 3.10+"></a>
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</p>
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MOOZY is a slide and patient-level foundation model for computational pathology. The patient case, not the individual slide, is the core unit of representation. A vision-only slide encoder pretrained with masked self-distillation on 77,134 public slides is aligned with clinical semantics through multi-task supervision over 333 tasks (205 classification, 128 survival) from 56 public datasets spanning 23 anatomical sites. A case transformer explicitly models dependencies across all slides from the same patient, replacing the naive early/late fusion used by prior methods. 85.77M total parameters. Trained entirely on public data.
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## Citation
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<!-- TODO: update arXiv ID when paper is posted -->
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```bibtex
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@article{kotp2026moozy,
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title = {MOOZY: A Patient-First Foundation Model for Computational Pathology},
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author = {Kotp, Yousef and Trinh, Vincent Quoc-Huy and Pal, Christopher and Hosseini, Mahdi S.},
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journal = {arXiv preprint arXiv:XXXX.XXXXX},
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year = {2026}
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}
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```
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