Selecting Regions of Interest in Large Multi-Scale Images for Cancer Pathology

TL;DR

This paper introduces a reinforcement learning-based method to efficiently identify regions of interest in large, multi-scale pathology images, aiding cancer diagnosis and classification.

Contribution

It presents a novel approach combining reinforcement learning and beam search to locate ROIs in high-resolution whole slide images for liver cancer detection.

Findings

Effective ROI detection in large WSIs

Improved accuracy in liver cancer subtype classification

Reduced computational load compared to exhaustive search

Abstract

Recent breakthroughs in object detection and image classification using Convolutional Neural Networks (CNNs) are revolutionizing the state of the art in medical imaging, and microscopy in particular presents abundant opportunities for computer vision algorithms to assist medical professionals in diagnosis of diseases ranging from malaria to cancer. High resolution scans of microscopy slides called Whole Slide Images (WSIs) offer enough information for a cancer pathologist to come to a conclusion regarding cancer presence, subtype, and severity based on measurements of features within the slide image at multiple scales and resolutions. WSIs' extremely high resolutions and feature scales ranging from gross anatomical structures down to cell nuclei preclude the use of standard CNN models for object detection and classification, which have typically been designed for images with dimensions in the hundreds of pixels and with objects on the order of the size of the image itself. We explore parallel approaches based on Reinforcement Learning and Beam Search to learn to progressively zoom into the WSI to detect Regions of Interest (ROIs) in liver pathology slides containing one of two types of liver cancer, namely Hepatocellular Carcinoma (HCC) and Cholangiocarcinoma (CC). These ROIs can then be presented directly to the pathologist to aid in measurement and diagnosis or be used for automated classification of tumor subtype.