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

Frei, Ana Leni; Khan, Amjad; Zens, Philipp; Lugli, Alessandro; Zlobec, Inti; Fischer, Andreas (28 April 2023). GammaFocus: An image augmentation method to focus model attention for classification. In: Medical Imaging with Deep Learning 2023.

Preview

Text 35_gammafocus_an_image_augmentati.pdf - Published Version Available under License Creative Commons: Attribution (CC-BY). Download (2MB) | Preview

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).

Interest & Impact

Downloads

0 since deposited on 04 Aug 2023
0 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item