Manuscript received September 18, 2025; revised November 7, 2025; accepted December 10, 2025; published March 26, 2026.

Abstract—Accurate tumor staging and risk stratification in breast cancer are critical for guiding treatment decisions. Traditional decision-making methods rely on Whole Slide Images (WSIs) analysis, which is labor-intensive and subject to inter-observer variability. To address these challenges in tumor staging, we propose a modular and interpretable deep learning framework for automated tumor staging through multi-resolution histopathological analysis. Our Hierarchical Multi-Scale Transformer (HMS-T) integrates the Vision Transformers (ViTs) operating at 5×, 10×, and 20× magnifications to capture both the cellular and architectural features. In addition, a novel cross-scale attention fusion module combines these multi-scale resolution representations, for enabling robust prediction of the American Joint Committee on Cancer (AJCC) stage group labels recorded in the clinical data from primary-tumor WSIs. Trained on 1092 patients from The Cancer Genome Atlas–Breast Invasive Carcinoma (TCGA-BRCA) cohort, our HMS-T achieves state-of-the-art performance with a staging accuracy of 91.5%, a macro F1-Score of 0.89, and a quadratic-weighted kappa of 0.92, which demonstrates the strong agreement with pathological standards. Moreover, our model’s attention maps exhibit high spatial interpretability, aligning closely with expert-annotated regions (dice score = 0.81). Ultimately, we introduced a lightweight clinical extension for the preliminary survival risk stratification, achieving a concordance index of 0.74, thereby bridging toward full prognostic modeling. By combining high performance with transparent decisionmaking, HMS-T represents a significant advancement toward deployable Artificial Intelligence (AI)-assisted pathology tools for breast cancer.

Keywords—multi-scale histopathology, vision transformers, cross-scale attention fusion, American Joint Committee on Cancer (AJCC) tumour staging, survival risk stratification, Whole Slide Images (WSIs), deep learning

Cite: Satyanarayana Reddy Beram, R Lalchhanhima, and Ksh. Robert Singh, "Hierarchical Multi-scale Transformer for Breast Tumor Staging with Visual Interpretability and Risk Stratification," Journal of Image and Graphics, Vol. 14, No. 2, pp. 208-229, 2026.

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC-BY-4.0).