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2019 | OriginalPaper | Buchkapitel

11. Deep Hashing and Its Application for Histopathology Image Analysis

verfasst von : Xiaoshuang Shi, Lin Yang

Erschienen in: Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

Verlag: Springer International Publishing

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Abstract

Content-based image retrieval (CBIR) has attracted considerable attention for histopathology image analysis because it can provide more clinical evidence to support the diagnosis. Hashing is an important tool in CBIR due to the significant gain in both computation and storage. Because of the tremendous success of deep learning, deep hashing simultaneously learning powerful feature representations and binary codes has achieved promising performance on microscopic images. This chapter presents several popular deep hashing techniques and their applications on histopathology images. It starts introducing the automated histopathology image analysis and explaining the reasons why deep hashing is a significant and urgent need for data analysis in histopathology images. Then, it specifically discusses three popular deep hashing techniques and mainly introduces pairwise-based deep hashing. Finally, it presents their applications on histopathology image analysis.

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Titel: Deep Hashing and Its Application for Histopathology Image Analysis
verfasst von: Xiaoshuang Shi
Lin Yang
Verlag: Springer International Publishing
Buch: Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics
Print ISBN: 978-3-030-13968-1

Electronic ISBN: 978-3-030-13969-8

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-13969-8_11

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