  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.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning