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Parallel multiple instance learning for extremely large histopathology image analysis

BACKGROUND: Histopathology images are critical for medical diagnosis, e.g., cancer and its treatment. A standard histopathology slice can be easily scanned at a high resolution of, say, 200,000×200,000 pixels. These high resolution images can make most existing imaging processing tools infeasible or...

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Autores principales: Xu, Yan, Li, Yeshu, Shen, Zhengyang, Wu, Ziwei, Gao, Teng, Fan, Yubo, Lai, Maode, Chang, Eric I-Chao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5543478/
https://www.ncbi.nlm.nih.gov/pubmed/28774262
http://dx.doi.org/10.1186/s12859-017-1768-8
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author Xu, Yan
Li, Yeshu
Shen, Zhengyang
Wu, Ziwei
Gao, Teng
Fan, Yubo
Lai, Maode
Chang, Eric I-Chao
author_facet Xu, Yan
Li, Yeshu
Shen, Zhengyang
Wu, Ziwei
Gao, Teng
Fan, Yubo
Lai, Maode
Chang, Eric I-Chao
author_sort Xu, Yan
collection PubMed
description BACKGROUND: Histopathology images are critical for medical diagnosis, e.g., cancer and its treatment. A standard histopathology slice can be easily scanned at a high resolution of, say, 200,000×200,000 pixels. These high resolution images can make most existing imaging processing tools infeasible or less effective when operated on a single machine with limited memory, disk space and computing power. RESULTS: In this paper, we propose an algorithm tackling this new emerging “big data” problem utilizing parallel computing on High-Performance-Computing (HPC) clusters. Experimental results on a large-scale data set (1318 images at a scale of 10 billion pixels each) demonstrate the efficiency and effectiveness of the proposed algorithm for low-latency real-time applications. CONCLUSIONS: The framework proposed an effective and efficient system for extremely large histopathology image analysis. It is based on the multiple instance learning formulation for weakly-supervised learning for image classification, segmentation and clustering. When a max-margin concept is adopted for different clusters, we obtain further improvement in clustering performance.
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spelling pubmed-55434782017-08-07 Parallel multiple instance learning for extremely large histopathology image analysis Xu, Yan Li, Yeshu Shen, Zhengyang Wu, Ziwei Gao, Teng Fan, Yubo Lai, Maode Chang, Eric I-Chao BMC Bioinformatics Research Article BACKGROUND: Histopathology images are critical for medical diagnosis, e.g., cancer and its treatment. A standard histopathology slice can be easily scanned at a high resolution of, say, 200,000×200,000 pixels. These high resolution images can make most existing imaging processing tools infeasible or less effective when operated on a single machine with limited memory, disk space and computing power. RESULTS: In this paper, we propose an algorithm tackling this new emerging “big data” problem utilizing parallel computing on High-Performance-Computing (HPC) clusters. Experimental results on a large-scale data set (1318 images at a scale of 10 billion pixels each) demonstrate the efficiency and effectiveness of the proposed algorithm for low-latency real-time applications. CONCLUSIONS: The framework proposed an effective and efficient system for extremely large histopathology image analysis. It is based on the multiple instance learning formulation for weakly-supervised learning for image classification, segmentation and clustering. When a max-margin concept is adopted for different clusters, we obtain further improvement in clustering performance. BioMed Central 2017-08-03 /pmc/articles/PMC5543478/ /pubmed/28774262 http://dx.doi.org/10.1186/s12859-017-1768-8 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Xu, Yan
Li, Yeshu
Shen, Zhengyang
Wu, Ziwei
Gao, Teng
Fan, Yubo
Lai, Maode
Chang, Eric I-Chao
Parallel multiple instance learning for extremely large histopathology image analysis
title Parallel multiple instance learning for extremely large histopathology image analysis
title_full Parallel multiple instance learning for extremely large histopathology image analysis
title_fullStr Parallel multiple instance learning for extremely large histopathology image analysis
title_full_unstemmed Parallel multiple instance learning for extremely large histopathology image analysis
title_short Parallel multiple instance learning for extremely large histopathology image analysis
title_sort parallel multiple instance learning for extremely large histopathology image analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5543478/
https://www.ncbi.nlm.nih.gov/pubmed/28774262
http://dx.doi.org/10.1186/s12859-017-1768-8
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