# Fast, Unsupervised Framework for Registration Quality Assessment of Multi-stain Histological Whole Slide Pairs

arXiv: https://arxiv.org/abs/2602.04046 · HTML: https://mlsanity.com/paper/2602.04046/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Builds on (most cited first, 3 of 3)

- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Efficient Quality Control of Whole Slide Pathology Images with Human-in-the-loop Training](https://mlsanity.com/paper/2409.19587/) (2024)
- [VIMs: Virtual Immunohistochemistry Multiplex staining via Text-to-Stain Diffusion Trained on Uniplex Stains](https://mlsanity.com/paper/2407.19113/) (2024)

## Abstract (arXiv)

High-fidelity registration of histopathological whole slide images (WSIs), such as hematoxylin & eosin (H&E) and immunohistochemistry (IHC), is vital for integrated molecular analysis but challenging to evaluate without ground-truth (GT) annotations. Existing WSI-level assessments -- using annotated landmarks or intensity-based similarity metrics -- are often time-consuming, unreliable, and computationally intensive, limiting large-scale applicability. This study proposes a fast, unsupervised framework that jointly employs down-sampled tissue masks- and deformations-based metrics for registration quality assessment (RQA) of registered H&E and IHC WSI pairs. The masks-based metrics measure global structural correspondence, while the deformations-based metrics evaluate local smoothness, continuity, and transformation realism. Validation across multiple IHC markers and multi-expert assessments demonstrate a strong correlation between automated metrics and human evaluations. In the absence of GT, this framework offers reliable, real-time RQA with high fidelity and minimal computational resources, making it suitable for large-scale quality control in digital pathology.