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.

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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).

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

04 Faculty of Medicine > Service Sector > Institute of Pathology > Clinical Pathology
04 Faculty of Medicine > Service Sector > Institute of Pathology

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

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

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health
000 Computer science, knowledge & systems
600 Technology > 620 Engineering

Language:

German

Submitter:

Ana Leni Frei

Date Deposited:

04 Aug 2023 07:19

Last Modified:

04 Aug 2023 07:19

Uncontrolled Keywords:

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

BORIS DOI:

10.48350/185209

URI:

https://boris.unibe.ch/id/eprint/185209

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