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Computer Science Department Colloquium

Towards Large-Scale Histopathological Image Analysis: Hashing-Based Image Retrieval

 

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Friday, January 23, 2015, 02:00pm

 

With the ever-increasing amount of annotated medical data, large-scale, data-driven methods provide the promise of bridging the semantic gap between images and diagnoses. The goal of our research project is to increase the scale at which interactive systems can be effective for knowledge discovery in potentially massive databases of medical images. Particularly, we focus on the automatic analysis of histopathological images, and propose a scalable image retrieval framework with high-dimensional features extracted in cell-level. We present a kernelized and supervised hashing method to bridge the semantic gap. With a small amount of supervised information, our method can compress a 10,000-dimensional image feature vector into only tens of binary bits with informative signatures preserved, and these binary codes are then indexed into a hash table that enables real-time retrieval. We validate the hashing-based image retrieval framework on several thousands of images of breast and lung microscopic tissues for both image classification and retrieval. Our framework achieves high search accuracy and promising computational efficiency, comparing favorably with other commonly used methods.

Speaker: Shaoting Zhang

Bio

Dr. Shaoting Zhang is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Charlotte. Before joining UNC Charlotte, he was a faculty member in the Department of Computer Science at Rutgers-New Brunswick (Rese

Location : CBIM Multipurpose Room ( Room 22 )

Committee

Dimitris Metaxas

Event Type: Computer Science Department Colloquium

Abstract: 

Organization

University of North Carolina