STAMP: Tumor Associative Modeling

Example Use Cases

• 1

  Predict biomarkers and patient outcomes from whole-slide histopathology images using weakly-supervised deep learning.

• 2

  A scalable and reproducible pipeline to process large multi-center pathology cohorts with minimal coding.

• 3

  Explainable AI outputs such as heatmaps and top-tile exports for model auditing and publication.

Description

STAMP provides a scalable, modular workflow for clinical researchers and machine-learning engineers to collaboratively discover and evaluate image-based biomarkers from gigapixel pathology slides without requiring pixel-level annotations. It supports multiple foundation models, multi-task learning, and generates explainable outputs like heatmaps, enabling reproducible computational pathology projects. The protocol is peer-reviewed and validated across multiple tumor types and centers.