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.

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

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.

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).

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

This repository includes 497 task definitions in the tasks/ directory. Each task has a config.yaml (task type, organ, label mapping) and a task.csv (annotations and splits). MOOZY was trained on 333 of them (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. The remaining 164 tasks are additional TCGA annotations (AJCC pathologic staging and molecular subtypes), and MOOZY was not trained on any of them.

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