GammaFocus: An image augmentation method to focus model attention for classification

Ana Leni Frei, Amjad Khan, Philipp Zens, Alessandro Lugli, Inti Zlobec, Andreas Fischer

Keywords: digital pathology, gamma correction, image augmentation, contrast enhancement, image classification

TL;DR: An image augmentation method based on gamma variations to focus model attention for classification

Abstract: In histopathology, histologic elements are not randomly located across an image but organize into structured patterns. In this regard, classification tasks or feature extraction from histology images may require context information to increase performance. In this work, we explore the importance of keeping context information for a cell classification task on Hematoxylin and Eosin (H$\&$E) scanned whole slide images (WSI) in colorectal cancer. We show that to differentiate normal from malignant epithelial cells, the environment around the cell plays a critical role. We propose here an image augmentation based on gamma variations to guide deep learning models to focus on the object of interest while keeping context information. This augmentation method yielded more specific models and helped to increase the model performance (weighted F1 score with/without gamma augmentation respectively, PanNuke: 99.49 vs 99.37 and TCGA: 91.38 vs. 89.12, $p<0.05$).