MOOZY: A Patient-First Foundation Model for Computational Pathology

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.

Table of Contents

• Installation
• Usage
  • From pre-computed H5 feature files
  • From raw whole-slide images
  • Python API
  • Arguments
  • Output format
• Architecture
• Tasks
• Citation
• License

Installation

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 and TRIDENT outputs.

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 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).

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

Python API

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

@article{kotp2026moozy,
  title   = {MOOZY: A Patient-First Foundation Model for Computational Pathology},
  author  = {Kotp, Yousef and Trinh, Vincent Quoc-Huy and Pal, Christopher and Hosseini, Mahdi S.},
  journal = {arXiv preprint arXiv:XXXX.XXXXX},
  year    = {2026}
}

License

CC BY-NC-SA 4.0. Research and non-commercial use only.