On the benefits of region of interest detection for whole slide image classification

Abstract

Whole slide image (WSI) classification methods typically use fixed-size patches that are processed separately and are aggregated for the final slide-level prediction. Image segmentation methods are designed to obtain a delineation of specific tissue types. These two tasks are usually studied independently. The aim of this work is to investigate the effect of region of interest (ROI) detection as a preliminary step for WSI classification. First, we process each WSI by using a pixel-level classifier that provides a binary segmentation mask for potentially important ROIs. We evaluate both single-resolution models that process each magnification independently and multi-resolution models that simultaneously incorporate contextual information and local details. Then, we compare the WSI classification performances of patch-based models when the patches used for both training and testing are extracted from the whole image and when they are sampled from only within the detected ROIs. The experiments using a binary classification setting for breast histopathology slides as benign vs. malignant show that the classifier that uses the patches sampled from the whole image achieves an F1 score of 0.68 whereas the classifiers that use patches sampled from the ROI detection results produced by the single- and multi-resolution models obtain scores between 0.75 and 0.83.