Histopathological Stain Transfer using Style Transfer Network with Adversarial Loss
This addresses domain shift issues in medical imaging for pathologists and researchers, but it is incremental as it builds on existing stain normalization and style transfer techniques.
The paper tackles the problem of poor deep learning model performance on histopathological images from different labs/scanners by proposing a stain normalization method using style transfer with adversarial loss and a novel HRNet-based generator, achieving better inference performance on Whole Slide Images from 8 labs compared to no normalization.
Deep learning models that are trained on histopathological images obtained from a single lab and/or scanner give poor inference performance on images obtained from another scanner/lab with a different staining protocol. In recent years, there has been a good amount of research done for image stain normalization to address this issue. In this work, we present a novel approach for the stain normalization problem using fast neural style transfer coupled with adversarial loss. We also propose a novel stain transfer generator network based on High-Resolution Network (HRNet) which requires less training time and gives good generalization with few paired training images of reference stain and test stain. This approach has been tested on Whole Slide Images (WSIs) obtained from 8 different labs, where images from one lab were treated as a reference stain. A deep learning model was trained on this stain and the rest of the images were transferred to it using the corresponding stain transfer generator network. Experimentation suggests that this approach is able to successfully perform stain normalization with good visual quality and provides better inference performance compared to not applying stain normalization.