Stain transfer using generative adversarial networks and disentangled features
Stain transfer using generative adversarial networks and disentangled features
Authors: Atefeh Ziaei Moghadam, Hamed Azarnoush, Seyyed Ali Seyyedsalehi, Mohammad Havaei
Publication date: 2022
Journal: Computers in Biology and Medicine
Publisher:
Pergamon
Description: With the digitization of histopathology, machine learning algorithms have been developed to help pathologists. Color variation in histopathology images degrades the performance of these algorithms. Many models have been proposed to resolve the impact of color variation and transfer histopathology images to a single stain style. Major shortcomings include manual feature extraction, bias on a reference image, being limited to one style to one style transfer, dependence on style labels for source and target domains, and information loss. We propose two models, considering these shortcomings. Our main novelty is using Generative Adversarial Networks (GANs) along with feature disentanglement. The models extract color-related and structural features with neural networks; thus, features are not hand-crafted. Extracting features helps our models do many-to-one stain transformations and require only target-style …
Total citations: 13
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