Deep Feature Representations for Variable-Sized Regions of Interest in Breast Histopathology.
Journal:
IEEE journal of biomedical and health informatics
Published Date:
Jun 3, 2021
Abstract
OBJECTIVE: Modeling variable-sized regions of interest (ROIs) in whole slide images using deep convolutional networks is a challenging task, as these networks typically require fixed-sized inputs that should contain sufficient structural and contextual information for classification. We propose a deep feature extraction framework that builds an ROI-level feature representation via weighted aggregation of the representations of variable numbers of fixed-sized patches sampled from nuclei-dense regions in breast histopathology images.
