README.md · AtlasAnalyticsLab/MOOZY at main

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---
license: cc-by-nc-sa-4.0
library_name: moozy
pipeline_tag: feature-extraction
base_model: 1aurent/vit_small_patch8_224.lunit_dino
tags:
  - 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
  - pytorch
  - transformer
datasets:
  - MahmoodLab/Patho-Bench
metrics:
  - f1
  - roc_auc
  - accuracy
language:
  - en
model-index:
  - name: MOOZY
    results:
      - task:
          type: image-classification
          name: Residual Cancer Burden Classification
        dataset:
          type: bc_therapy
          name: BC Therapy
        metrics:
          - type: f1
            value: 0.56
            name: Weighted F1
          - type: roc_auc
            value: 0.74
            name: Weighted ROC-AUC
          - type: accuracy
            value: 0.51
            name: Balanced Accuracy
      - task:
          type: image-classification
          name: TP53 Mutation Prediction
        dataset:
          type: cptac_brca
          name: CPTAC-BRCA
        metrics:
          - type: f1
            value: 0.87
            name: Weighted F1
          - type: roc_auc
            value: 0.86
            name: Weighted ROC-AUC
          - type: accuracy
            value: 0.86
            name: Balanced Accuracy
      - task:
          type: image-classification
          name: BAP1 Mutation Prediction
        dataset:
          type: cptac_ccrcc
          name: CPTAC-CCRCC
        metrics:
          - type: f1
            value: 0.89
            name: Weighted F1
          - type: roc_auc
            value: 0.79
            name: Weighted ROC-AUC
          - type: accuracy
            value: 0.78
            name: Balanced Accuracy
      - task:
          type: image-classification
          name: ACVR2A Mutation Prediction
        dataset:
          type: cptac_coad
          name: CPTAC-COAD
        metrics:
          - type: f1
            value: 0.91
            name: Weighted F1
          - type: roc_auc
            value: 0.91
            name: Weighted ROC-AUC
          - type: accuracy
            value: 0.90
            name: Balanced Accuracy
      - task:
          type: image-classification
          name: Histologic Grade Classification
        dataset:
          type: cptac_lscc
          name: CPTAC-LSCC
        metrics:
          - type: f1
            value: 0.78
            name: Weighted F1
          - type: roc_auc
            value: 0.75
            name: Weighted ROC-AUC
          - type: accuracy
            value: 0.77
            name: Balanced Accuracy
      - task:
          type: image-classification
          name: KRAS Mutation Prediction
        dataset:
          type: cptac_luad
          name: CPTAC-LUAD
        metrics:
          - type: f1
            value: 0.85
            name: Weighted F1
          - type: roc_auc
            value: 0.80
            name: Weighted ROC-AUC
          - type: accuracy
            value: 0.79
            name: Balanced Accuracy
      - task:
          type: image-classification
          name: IDH Status Classification
        dataset:
          type: ebrains
          name: EBRAINS
        metrics:
          - type: f1
            value: 0.97
            name: Weighted F1
          - type: roc_auc
            value: 0.99
            name: Weighted ROC-AUC
          - type: accuracy
            value: 0.97
            name: Balanced Accuracy
      - task:
          type: image-classification
          name: Treatment Response Prediction
        dataset:
          type: mbc
          name: MBC
        metrics:
          - type: f1
            value: 0.58
            name: Weighted F1
          - type: roc_auc
            value: 0.68
            name: Weighted ROC-AUC
          - type: accuracy
            value: 0.48
            name: Balanced Accuracy
---

# MOOZY: A Patient-First Foundation Model for Computational Pathology

<p align="center">
  <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>
  <a href="https://arxiv.org/abs/2603.27048"><img src="https://img.shields.io/badge/arXiv-2603.27048-B31B1B?logo=arxiv" alt="arXiv"></a>
  <a href="https://github.com/AtlasAnalyticsLab/MOOZY"><img src="https://img.shields.io/badge/GitHub-Repository-181717?logo=github" alt="GitHub"></a>
  <!-- TODO: update PyPI badge once first version is published -->
  <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>
  <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>
  <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>
</p>

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.

![MOOZY data scale](assets/data_scale_overview.png)

## Table of Contents

- [Installation](#installation)
- [Usage](#usage)
  - [From pre-computed H5 feature files](#from-pre-computed-h5-feature-files)
  - [From raw whole-slide images](#from-raw-whole-slide-images)
  - [Python API](#python-api)
  - [Arguments](#arguments)
  - [Output format](#output-format)
- [Architecture](#architecture)
- [Tasks](#tasks)
- [Citation](#citation)
- [License](#license)

## Installation

```bash
pip install moozy
```

The checkpoint and task definitions are downloaded automatically from this repository on first use.

## Usage

### From pre-computed H5 feature files

The faster path. Pass `.h5` files containing patch features extracted with `lunit_vit_small_patch8_dino` at 224x224 patch size. Compatible with [AtlasPatch](https://github.com/AtlasAnalyticsLab/AtlasPatch) and [TRIDENT](https://github.com/mahmoodlab/TRIDENT) outputs.

```bash
moozy encode slide_1.h5 slide_2.h5 --output case_embedding.h5
```

### From raw whole-slide images

Pass slide files directly (`.svs`, `.tiff`, `.ndpi`, `.mrxs`, etc.). MOOZY calls [AtlasPatch](https://github.com/AtlasAnalyticsLab/AtlasPatch) under the hood to segment tissue, extract patches, and compute features. Requires `atlas-patch`, `sam2`, and the OpenSlide system library (see the [AtlasPatch installation guide](https://github.com/AtlasAnalyticsLab/AtlasPatch#installation)).

```bash
moozy encode slide_1.svs slide_2.svs --output case_embedding.h5 --target_mag 20
```

### Python API

```python
from moozy.encoding import run_encoding

# From H5 feature files
run_encoding(
    slide_paths=["slide_1.h5", "slide_2.h5"],
    output_path="case_embedding.h5",
)

# From raw slides
run_encoding(
    slide_paths=["slide_1.svs", "slide_2.svs"],
    output_path="case_embedding.h5",
    target_mag=20,
)
```

### Arguments

| Argument | Default | Description |
|----------|---------|-------------|
| `SLIDES` | (required) | One or more H5 feature files or raw slide files forming a single case. Cannot mix the two types. |
| `--output`, `-o` | (required) | Output H5 file path. |
| `--mixed_precision` | off | Enable bfloat16 mixed precision. |
| `--target_mag` | 20 | Magnification for patch extraction from raw slides. Ignored for H5. |
| `--step_size` | 224 | Stride between patch centers in pixels. Set < 224 for overlap. Ignored for H5. |
| `--mpp_csv` | - | CSV with `wsi,mpp` columns for microns-per-pixel overrides. Ignored for H5. |

### Output format

The output H5 file contains a `features` dataset (768-D float32 case embedding) and a `coords` dataset with slide metadata.

## Architecture

| Component | Architecture | Params | Output dim |
|-----------|-------------|--------|------------|
| Patch encoder | ViT-S/8 (Lunit DINO) | 21.67M | 384 |
| Slide encoder | ViT, 6 layers, 768-D, 12 heads, 2D ALiBi | 42.8M | 768 |
| Case transformer | 3 layers, 12 heads | 21.3M | 768 |

## Tasks

This repository includes 333 task definitions in the `tasks/` directory. Each task has a `config.yaml` (task type, organ, label mapping) and a `task.csv` (annotations and splits). The tasks cover 205 classification and 128 survival endpoints across all 32 TCGA cohorts, all 10 CPTAC cohorts, REG, BC-Therapy, BRACS, CAMELYON17, DHMC Kidney, DHMC LUAD, EBRAINS, IMP Colorectum, IMP Cervix, MBC, MUT-HET-RCC, NADT Prostate, NAT-BRCA, and PANDA.

## Citation

```bibtex
@misc{kotp2026moozypatientfirstfoundationmodel,
      title={MOOZY: A Patient-First Foundation Model for Computational Pathology},
      author={Yousef Kotp and Vincent Quoc-Huy Trinh and Christopher Pal and Mahdi S. Hosseini},
      year={2026},
      eprint={2603.27048},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2603.27048},
}
```

## License

[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). Research and non-commercial use only.