Structure-Preserving Multi-Domain Stain Color Augmentation using Style-Transfer with Disentangled Representations
This addresses poor generalization of deep learning models in digital pathology due to stain color variations across laboratories, offering a domain-specific solution.
The paper tackled the problem of color variations in digital pathology images affecting deep learning generalization by proposing HistAuGAN, a color augmentation technique that simulates realistic histology stain colors, and demonstrated it outperforms conventional methods on the Camelyon17 dataset by mitigating batch effects.
In digital pathology, different staining procedures and scanners cause substantial color variations in whole-slide images (WSIs), especially across different laboratories. These color shifts result in a poor generalization of deep learning-based methods from the training domain to external pathology data. To increase test performance, stain normalization techniques are used to reduce the variance between training and test domain. Alternatively, color augmentation can be applied during training leading to a more robust model without the extra step of color normalization at test time. We propose a novel color augmentation technique, HistAuGAN, that can simulate a wide variety of realistic histology stain colors, thus making neural networks stain-invariant when applied during training. Based on a generative adversarial network (GAN) for image-to-image translation, our model disentangles the content of the image, i.e., the morphological tissue structure, from the stain color attributes. It can be trained on multiple domains and, therefore, learns to cover different stain colors as well as other domain-specific variations introduced in the slide preparation and imaging process. We demonstrate that HistAuGAN outperforms conventional color augmentation techniques on a classification task on the publicly available dataset Camelyon17 and show that it is able to mitigate present batch effects.