Multimarginal Wasserstein Barycenter for Stain Normalization and Augmentation
Not on the map. Why?
Abstract (arXiv)
Variations in hematoxylin and eosin (H&E) stained images (due to clinical lab protocols, scanners, etc) directly impact the quality and accuracy of clinical diagnosis, and hence it is important to control for these variations for a reliable diagnosis. In this work, we present a new approach based on the multimarginal Wasserstein barycenter to normalize and augment H&E stained images given one or more references. Specifically, we provide a mathematically robust way of naturally incorporating additional images as intermediate references to drive stain normalization and augmentation simultaneously. The presented approach showed superior results quantitatively and qualitatively as compared to state-of-the-art methods for stain normalization. We further validated our stain normalization and augmentations in the nuclei segmentation task on a publicly available dataset, achieving state-of-the-art results against competing approaches.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jun 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 2; 2023: 0; 2024: 0; 2025: 1.
6 papers.
- 2022RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalizationcited by 32
- 2021Structure-Preserving Multi-Domain Stain Color Augmentation using Style-Transfer with Disentangled Representationscited by 31
- 2021Landmarks Augmentation with Manifold-Barycentric Oversamplingcited by 4
- 2022Stain-invariant self supervised learning for histopathology image analysiscited by 4
- 2025Differentially Private Wasserstein Barycenterscited by 2
- 2021ALLWAS: Active Learning on Language models in WASserstein spacecited by 1
- 2025Differentially Private Wasserstein Barycenters3 Oct 2025
- 2022Stain-invariant self supervised learning for histopathology image analysis14 Nov 2022
- 2022RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalization25 Jun 2022
- 2021ALLWAS: Active Learning on Language models in WASserstein space3 Sep 2021
- 2021Structure-Preserving Multi-Domain Stain Color Augmentation using Style-Transfer with Disentangled Representations26 Jul 2021
- 2021Landmarks Augmentation with Manifold-Barycentric Oversampling2 Apr 2021