# HASD: Hierarchical Adaption for pathology Slide-level Domain-shift

arXiv: https://arxiv.org/abs/2506.23673 · HTML: https://mlsanity.com/paper/2506.23673/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 30 Jun 2025.

## Builds on (most cited first, 14 of 14)

- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival Prediction](https://mlsanity.com/paper/2306.08330/) (2023)
- [BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix](https://mlsanity.com/paper/2204.11425/) (2022)
- [Current Pathology Foundation Models are unrobust to Medical Center Differences](https://mlsanity.com/paper/2501.18055/) (2025)
- [Hierarchical Optimal Transport for Comparing Histopathology Datasets](https://mlsanity.com/paper/2204.08324/) (2022)
- [MaNi: Maximizing Mutual Information for Nuclei Cross-Domain Unsupervised Segmentation](https://mlsanity.com/paper/2206.14437/) (2022)
- [Domain adaptation using optimal transport for invariant learning using histopathology datasets](https://mlsanity.com/paper/2303.02241/) (2023)
- [Detecting Domain Shift in Multiple Instance Learning for Digital Pathology Using Fr'echet Domain Distance](https://mlsanity.com/paper/2405.09934/) (2024)
- [Unsupervised Latent Stain Adaptation for Computational Pathology](https://mlsanity.com/paper/2406.19081/) (2024)
- [Prior-RadGraphFormer: A Prior-Knowledge-Enhanced Transformer for Generating Radiology Graphs from X-Rays](https://mlsanity.com/paper/2303.13818/) (2023)
- [Enhancing Whole Slide Image Classification through Supervised Contrastive Domain Adaptation](https://mlsanity.com/paper/2412.04260/) (2024)
- [NeRF-based CBCT Reconstruction needs Normalization and Initialization](https://mlsanity.com/paper/2506.19742/) (2025)

## Built on by (most cited first, 6 of 6)

- [From Pixels to Pathology: Restoration Diffusion for Diagnostic-Consistent Virtual IHC](https://mlsanity.com/paper/2508.02528/) (2025)
- [Attention Pooling Enhances NCA-based Classification of Microscopy Images](https://mlsanity.com/paper/2508.12324/) (2025)
- [Towards Cellular-Scale Interpretability in Pathology Foundation Models for Biomarker Assessment](https://mlsanity.com/paper/2511.05150/) (2025)
- [Discrepancy Minimization Improves Cross-Hospital Robustness in Digital Pathology](https://mlsanity.com/paper/2605.25175/) (2026)
- [Semantic-Anchored Evidential Fusion for Domain-Robust Whole-Slide Survival Analysis](https://mlsanity.com/paper/2606.19966/) (2026)
- [How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging](https://mlsanity.com/paper/2608.12035/) (2026)

## Abstract (arXiv)

Domain shift is a critical problem for pathology AI as pathology data is heavily influenced by center-specific conditions. Current pathology domain adaptation methods focus on image patches rather than WSI, thus failing to capture global WSI features required in typical clinical scenarios. In this work, we address the challenges of slide-level domain shift by proposing a Hierarchical Adaptation framework for Slide-level Domain-shift (HASD). HASD achieves multi-scale feature consistency and computationally efficient slide-level domain adaptation through two key components: (1) a hierarchical adaptation framework that integrates a Domain-level Alignment Solver for feature alignment, a Slide-level Geometric Invariance Regularization to preserve the morphological structure, and a Patch-level Attention Consistency Regularization to maintain local critical diagnostic cues; and (2) a prototype selection mechanism that reduces computational overhead. We validate our method on two slide-level tasks across five datasets, achieving a 4.1% AUROC improvement in a Breast Cancer HER2 Grading cohort and a 3.9% C-index gain in a UCEC survival prediction cohort. Our method provides a practical and reliable slide-level domain adaption solution for pathology institutions, minimizing both computational and annotation costs.