Histopathological Image Analysis with Style-Augmented Feature Domain Mixing for Improved Generalization
This work addresses the challenge of improving generalization for medical diagnosis using histopathological images, though it is incremental as it builds on existing style transfer-based data augmentation methods.
The paper tackles the problem of domain generalization in histopathological image analysis by proposing a feature domain style mixing technique using adaptive instance normalization, which performs similarly or better than existing methods while requiring less computation and time.
Histopathological images are essential for medical diagnosis and treatment planning, but interpreting them accurately using machine learning can be challenging due to variations in tissue preparation, staining and imaging protocols. Domain generalization aims to address such limitations by enabling the learning models to generalize to new datasets or populations. Style transfer-based data augmentation is an emerging technique that can be used to improve the generalizability of machine learning models for histopathological images. However, existing style transfer-based methods can be computationally expensive, and they rely on artistic styles, which can negatively impact model accuracy. In this study, we propose a feature domain style mixing technique that uses adaptive instance normalization to generate style-augmented versions of images. We compare our proposed method with existing style transfer-based data augmentation methods and found that it performs similarly or better, despite requiring less computation and time. Our results demonstrate the potential of feature domain statistics mixing in the generalization of learning models for histopathological image analysis.