Journal article
Supervised graph hashing for histopathology image retrieval and classification
Medical image analysis, v 42, pp 117-128
Dec 2017
PMID: 28783503
Abstract
•An framework based on cell encoding for large-scale histopathological image analysis is proposed.•A supervised graph-based model via asymmetric relaxation and its scalable version are proposed.•A group-to-group matching method to retrieve images based on binary codes of cells is proposed.
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In pathology image analysis, morphological characteristics of cells are critical to grade many diseases. With the development of cell detection and segmentation techniques, it is possible to extract cell-level information for further analysis in pathology images. However, it is challenging to conduct efficient analysis of cell-level information on a large-scale image dataset because each image usually contains hundreds or thousands of cells. In this paper, we propose a novel image retrieval based framework for large-scale pathology image analysis. For each image, we encode each cell into binary codes to generate image representation using a novel graph based hashing model and then conduct image retrieval by applying a group-to-group matching method to similarity measurement. In order to improve both computational efficiency and memory requirement, we further introduce matrix factorization into the hashing model for scalable image retrieval. The proposed framework is extensively validated with thousands of lung cancer images, and it achieves 97.98% classification accuracy and 97.50% retrieval precision with all cells of each query image used.
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Details
- Title
- Supervised graph hashing for histopathology image retrieval and classification
- Creators
- Xiaoshuang Shi - University of FloridaFuyong Xing - University of FloridaKaiDi Xu - University of FloridaYuanpu Xie - University of FloridaHai Su - J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, 32611-6130, U.S.ALin Yang - University of Florida
- Publication Details
- Medical image analysis, v 42, pp 117-128
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Web of Science ID
- WOS:000415778100008
- Scopus ID
- 2-s2.0-85026733987
- Other Identifier
- 991021871357604721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Interdisciplinary Applications
- Engineering, Biomedical
- Radiology, Nuclear Medicine & Medical Imaging