Mining histopathological images via composite hashing and online learning.

Abstract

With a continuous growing amount of annotated histopathological images, large-scale and data-driven methods potentially provide the promise of bridging the semantic gap between these images and their diagnoses. The purpose of this paper is to increase the scale at which automated systems can entail scalable analysis of histopathological images in massive databases. Specifically, we propose a principled framework to unify hashing-based image retrieval and supervised learning. Concretely, composite hashing is designed to simultaneously fuse and compress multiple high-dimensional image features into tens of binary hash bits, enabling scalable image retrieval with a very low computational cost. Upon a local data subset that retains the retrieved images, supervised learning methods are applied on-the-fly to model image structures for accurate classification. Our framework is validated thoroughly on 1120 lung microscopic tissue images by differentiating adenocarcinoma and squamous carcinoma. The average accuracy as 87.5% with only 17ms running time, which compares favorably with other commonly used methods.