MOOZY: A Patient-First Foundation Model for Computational Pathology

Computational pathology needs whole-slide image (WSI) foun-dation models that transfer across diverse clinical tasks, yet current ap-proaches remain largely slide-centric, often depend on private data andexpensive paired-report supervision, and do not explicitly model relation-ships among multiple slides from the same patient. We present MOOZY,a patient-first pathology foundation model in which the patient case, notthe individual slide, is the core unit of representation. MOOZY explicitlymodels dependencies across all slides from the same patient via a casetransformer during pretraining, combining multi-stage self-supervisionwith scaled low-cost task supervision. In Stage 1, we pretrain a vision-only slide encoder on 77,134 public slide feature grids using masked self-distillation. In Stage 2, we align these representations with clinical se-mantics using a case transformer and multi-task supervision over 333tasks from 56 public datasets, including 205 classification and 128 sur-vival tasks across four endpoints. Across sixteen held-out tasks, MOOZYimproves macro weighted F1, balanced accuracy, and macro weightedROC-AUC relative to PRISM by +4.19%, +7.93%, and +6.95%, re-spectively. MOOZY is also parameter efficient with 85.77M parameters,14× smaller than GigaPath. These results suggest that patient-level pre-training yields transferable embeddings, providing a path toward scalablepatient-first histopathology foundation models.