Abstract

Computer-aided diagnosis of medical images requires thorough analysis of image details. For example, examining all cells enables fifine-grained categorization of histopathological images. Traditional computational methods may have effiffifficiency issues when performing such detailed analysis. In this paper, we propose a robust and scalable solution to achieve this. Specififically, a robust segmentation method is developed to delineate region-of-interests (e.g., cells) accurately, using hierarchical voting and repulsive active contour. A hashingbased large-scale retrieval approach is also designed to examine and classify them by comparing with a massive training database. We evaluate this proposed framework on a challenging and important clinical use case, i.e., difffferentiation of two types of lung cancers (the adenocarcinoma and the squamous carcinoma), using thousands of histopathological images extracted from hundreds of patients. Our method has achieved promising performance, i.e., 87.3% accuracy and 1.68 seconds by searching among half-million cells.