Discovering Anomalous Patterns in Large Digital Pathology Images

Advances in medical imaging technology have created opportunities for computer-aided diagnostic tools to assist human practitioners in identifying relevant patterns in massive, multi-scale digital pathology slides. This work presents Hierarchical Linear Time Subset Scanning (HLTSS), a novel pattern detection method which exploits the hierarchical structure inherent in data produced through virtual microscopy in order to accurately and quickly identify regions of interest for pathologists to review. We take a digital image at various resolution levels, identify the most anomalous regions at a coarse level, and continue to analyze the data at increasingly granular resolutions until we accurately identify its most anomalous sub-regions. We demonstrate the performance of our novel method in identifying cancerous locations on digital bslides of prostate biopsy samples, and show that our methods detect regions of cancer in a few minutes with high accuracy both as measured by the ROC curve (measuring ability to distinguish between benign and cancerous slides) and by the spatial precision-recall curve (measuring ability to pick out the malignant areas on a slide which contains cancer).