Artifact Removal in Histopathology Images
Cameron Dahan, Stergios Christodoulidis, Maria Vakalopoulou, Joseph, Boyd
TL;DR
This paper introduces a weakly-supervised extension to CycleGAN for artifact removal in histopathology whole-slide images, addressing surjection issues and demonstrating promising results on a pan-cancer dataset.
Contribution
The paper proposes a novel weakly-supervised CycleGAN extension specifically designed for artifact removal in histopathology images, overcoming surjection problems.
Findings
Effective artifact removal demonstrated on TCGA dataset
Addresses surjection problem in image translation networks
Promising results in pan-cancer histopathology images
Abstract
In the clinical setting of histopathology, whole-slide image (WSI) artifacts frequently arise, distorting regions of interest, and having a pernicious impact on WSI analysis. Image-to-image translation networks such as CycleGANs are in principle capable of learning an artifact removal function from unpaired data. However, we identify a surjection problem with artifact removal, and propose an weakly-supervised extension to CycleGAN to address this. We assemble a pan-cancer dataset comprising artifact and clean tiles from the TCGA database. Promising results highlight the soundness of our method.
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Taxonomy
TopicsAI in cancer detection · Radiomics and Machine Learning in Medical Imaging · Colorectal Cancer Screening and Detection
MethodsResidual Connection · *Communicated@Fast*How Do I Communicate to Expedia? · Cycle Consistency Loss · GAN Least Squares Loss · Batch Normalization · HuMan(Expedia)||How do I get a human at Expedia? · PatchGAN · Instance Normalization · Tanh Activation · Sigmoid Activation