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Feature Learning with Multi-Stage Vision Transformers on Inter-Modality HER2 Status Scoring and Tumor Classification on Whole Slides

arXiv 2512.22335 ↗First posted 26 Dec 2025cs.CV cs.AIcited by 0

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Abstract (arXiv)

The popular use of histopathology images, such as hematoxylin and eosin (H&E), has proven to be useful in detecting tumors. However, moving such cancer cases forward for treatment requires accurate on the amount of the human epidermal growth factor receptor 2 (HER2) protein expression. Predicting both the lower and higher levels of HER2 can be challenging. Moreover, jointly analyzing H&E and immunohistochemistry (IHC) stained images for HER2 scoring is difficult. Although several deep learning methods have been investigated to address the challenge of HER2 scoring, they suffer from providing a pixel-level localization of HER2 status. In this study, we propose a single end-to-end pipeline using a system of vision transformers with HER2 status scoring on whole slide images of WSIs. The method includes patch-wise processing of H&E WSIs for tumor localization. A novel mapping function is proposed to correspondingly identify correlated IHC WSIs regions with malignant regions on H&E. A clinically inspired HER2 scoring mechanism is embedded in the pipeline and allows for automatic pixel-level annotation of 4-way HER2 scoring (0, 1+, 2+, and 3+). Also, the proposed method accurately returns HER2-negative and HER2-positive. Privately curated datasets were collaboratively extracted from 13 different cases of WSIs of H&E and IHC. A thorough experiment is conducted on the proposed method. Results obtained showed a good classification accuracy during tumor localization. Also, a classification accuracy of 0.94 and a specificity of 0.933 were returned for the prediction of HER2 status, scoring in the 4-way methods. The applicability of the proposed pipeline was investigated using WSIs patches as comparable to human pathologists. Findings from the study showed the usability of jointly evaluated H&E and IHC images on end-to-end ViTs-based models for HER2 scoring

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 50 references that are in the map. First posted on arXiv 26 Dec 2025.

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References · 11

  1. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  2. 2021Per-Pixel Classification is Not All You Need for Semantic Segmentationcited by 1,148
  3. 2023Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistlescited by 287
  4. 2021Vision Transformers with Hierarchical Attentioncited by 31
  5. 2023Predicting Ki67, ER, PR, and HER2 Statuses from H&E-stained Breast Cancer Imagescited by 14
  6. 2023Vision Transformers for Small Histological Datasets Learned through Knowledge Distillationcited by 10
  7. 2024HER2 and FISH Status Prediction in Breast Biopsy H&E-Stained Images Using Deep Learningcited by 5
  8. 2024DIOR-ViT: Differential Ordinal Learning Vision Transformer for Cancer Classification in Pathology Imagescited by 4
  9. 2023Hierarchical Vision Transformers for Context-Aware Prostate Cancer Grading in Whole Slide Imagescited by 3
  10. 2024Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Samplingcited by 3
  11. 2022Interpretable HER2 scoring by evaluating clinical Guidelines through a weakly supervised, constrained Deep Learning Approachcited by 2

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