Histopathological Image Analysis with Style-Augmented Feature Domain
  Mixing for Improved Generalization
Vaibhav Khamankar Sutanu Bera Saumik Bhattacharya Debashis Sen Prabir Kumar Biswas
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.