DaX: Learning General Pathology Representations Across Scales

• URL: http://arxiv.org/abs/2606.06983v1
• Date: Fri, 05 Jun 2026 07:21:11 GMT
• Title: DaX: Learning General Pathology Representations Across Scales
• Abstract summary: DaX is a pathology vision foundation model that adapts DINOv3-style self-supervised learning to whole-slide histopathology.<n>We construct a WSI-level benchmark comprising 161 clinically meaningful tasks from 44 public datasets.<n>DaX achieves the highest mean performance across tasks and consistently strong task-level ranking scores.
• Score: 11.473742972775455
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computational pathology requires visual representations that transfer across diverse clinical endpoints and remain robust to variation in magnification, staining, scanner type, slide preparation, and input resolution. We present DaX, a pathology vision foundation model that adapts DINOv3-style self-supervised learning to whole-slide histopathology. DaX is initialized from natural-image DINOv3 weights and incorporates continuous magnification training, cross-scale tissue views, orientation-agnostic and acquisition-robust augmentation, multi-input-size training, and Gram-anchored dense consistency. These designs aim to connect local cellular morphology with global tissue architecture while stabilizing dense token-level representations across input scales. We further construct a WSI-level benchmark comprising 161 clinically meaningful tasks from 44 public datasets, covering 28,182 patients and 34,394 slides across four clinical domains and nine task categories. All models are evaluated under a fixed patient-level cross-validation protocol with fold-level statistical ranking, enabling reproducible comparisons that are less sensitive to split-dependent variation. Across this benchmark, DaX achieves the highest mean performance across tasks and consistently strong task-level ranking scores, with gains spanning diagnostic pathology, biomarker and molecular profiling, tissue/specimen context, and risk, response, and prognosis. These results support DaX as a transferable visual encoder for computational pathology and provide a standardized evaluation framework for future pathology foundation models. Project page: https://alibaba-damo-academy.github.io/DaX/benchboard/.

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