Stain Normalization and Augmentation in Frequency Space for Histology Analysis.

Histopathology plays an indispensable role in cancer diagnosis. However, the frequent staining variations observed in tissue slides pose significant challenges to the effectiveness of computer-aided diagnostic systems. Stain Normalization (SN) and Stain Augmentation (SA) have been proposed to mitigate these issues. Traditional SN techniques focused on minimizing variations between slides, whereas SA sought to enhance data distribution without changing the inherent morphology, mainly within the image domain. Notably, many medical signals originate from frequency information, often processed in specialized facilities. Motivated by this, we introduce a novel approach: harnessing the frequency domain for SN and SA. Our proposed Fourier Stain Normalization (F-SN) and Fourier Stain Augmentation (FSA) methods innovatively mark a significant departure from conventional techniques by leveraging the focus on the frequency domain. Through the application of Fourier transformation, we transmute time-domain signals into the frequency domain, resulting in a streamlined and more efficient preprocessing phase compared to time-domain-based methodologies. Enhancing this approach, we incorporate a learnable mask prediction mechanism, adeptly merging source and target images, which bolsters its adaptability for a spectrum of downstream tasks. Comprehensive evaluations spanning medical image classification and segmentation using diverse network architectures, affirm the superiority of our F-SN and F-SA techniques over traditional time-domain methods, underscoring the frequency domain’s richer informational content. The code is available at https://github.com/Windyskys/FSNFSA.