Interactive Learning for Assisting Whole Slide Image Annotation

Abstract

Owing to the large dimensions of the histopathology whole slide images (WSI), visually searching for clinically significant regions (patches) is a tedious task for a medical expert. Sequential analysis of several such images further increases the workload resulting in poor diagnosis. A major impediment towards automating this task using deep learning models is that it requires a huge chunk of laboriously annotated data in the form of WSI patches. Our work suggests a novel CNN-based, expert feedback-driven interactive learning technique to mitigate this issue. The proposed method seeks to acquire labels of the most informative patches in small increments with multiple feedback rounds to maximize the throughput. It requires the expert to query a patch of interest from one slide and provide feedback to a set of unlabelled patches chosen using the proposed sampling strategy from a ranked list. The experiments on a large patient cohort of colorectal cancer histological patches (100K images with nine classes of tissues) show a significant reduction ($\approx 95\%$) in the amount of labelled data required to achieve state-of the-art results when compared to other existing interactive learning methods (35%–50%). We also demonstrate the utility of the proposed technique to assist a WSI tumor segmentation annotation task using the ICIAR breast cancer challenge dataset ($\approx 12.5$K patches per slide). The proposed technique reduces the scanning and searching area to about $2\%$ of the total area of WSI (by seeking labels of $\approx 250$ informative patches only) and achieves segmentation outputs with $85\%$ IOU. Thus our work helps avoid the routine procedure of exhaustive scanning and searching during annotation and diagnosis in general.