腾讯 AI Lab 官网

腾讯 AI Lab 官网
Scalable Mammogram Retrieval Using Composite Anchor Graph Hashing with Iterative Quantization
Abstract View the Paper
Content-Based Image Retrieval (CBIR) shows great significance in clinical decision-making, which explores the visual content of medical images rather than keywords, tags, or descriptions. It provides doctors an image-guided approach to explore relevant cases that could offer doctors instructive reference. Mammogram screening has been known to be widely used in the early-stage diagnosis of breast cancer, and could reduce its morbidity and mortality. In this paper, we aim to develop a scalable CBIR method for a large repository of mammogram. To this end, we extend the original Anchor Graph Hashing (AGH) and propose a new unsupervised hashing algorithm, named as Composite Anchor Graph Hashing with Iterative Quantization (CAGH- ITQ), which compresses mammographic ROIs into compact binary codes and enables real-time searching in Hamming space. Multimodal features and different distance metrics are integrated, performing upon a composite Anchor Graph. To improve the effectiveness of the hash code, quantization error is further iteratively minimized by introducing an orthogonal rotation matrix. We evaluate the presented C-AGH-ITQ algorithm on a dataset of 11; 533 mammographic ROIs obtained from the Digital Database for Screening Mammography (DDSM). Our method obtains more than 84% retrieval precision and 93% classification accuracy (by using k-NN prediction), which demonstrates that hash codes produced by C-AGH-ITQ well capture the visual similarities between mammographic images. In addition, since C-AGH-ITQ ensures linear complexity of the training procedure and constant time for query, our system is readily applicable to large-scale mammogram databases and has the potential to provide abundant clinical cases as reference.
Venue
IEEE Transactions on Circuits and Systems for Video Technology
Publication Time
2016
Authors
Jingjing Liu, Shaoting Zhang, Wei Liu, Cheng Deng, Yuanjie Zheng, and Dimitris N. Metaxas