Name
Title | ||
|---|---|---|
Disentangled Representation Learning Based Multidomain Stain Normalization For Histological Images |
Abstract | ||
|---|---|---|
Color variations of histological images due to multi-factor hinder the performance of computer-aided diagnosis (CAD) systems. Previous stain normalization methods have achieved excellent results. While in practice, a multidomain stain normalization method is still be needed when more than two color variations exist in dataset. In this paper, we propose a multidomain stain normalization model inspired by MUNIT [1], with the idea of disentangling the representations of content and style. We assume that the latent space of histological images can be decomposed into domain-shared content space and domain-specific style space. The stain normalization aims to transfer the styles cross domains and maintain the contents. In addition, we propose to use the earth mover’s distance(EMD) to evaluate the effectiveness of stain normalization. We evaluate our approach against the state-of-the-art methods quantitatively and qualitatively. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICIP40778.2020.9190757 | 2020 IEEE International Conference on Image Processing (ICIP) |
Keywords | DocType | ISSN |
Image color analysis,Image reconstruction,Generative adversarial networks,Training,Decoding,Generators,Biomedical imaging | Conference | 1522-4880 |
ISBN | Citations | PageRank |
978-1-7281-6395-6 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yao Xiang | 1 | 0 | 0.34 |
| Jialin Chen | 2 | 1 | 1.04 |
| Qing Liu | 3 | 19 | 3.99 |
| Yixiong Liang | 4 | 5 | 3.41 |