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Title: Deep cascaded networks for sparsely distributed object detection from medical images
Authors: Chen, H
Dou, Q
Yu, L
Qin, J 
Zhao, L
Mok, VCT
Wang, D
Shi, L
Heng, PA
Keywords: 3D deep learning
Computer aided diagnosis
Convolutional neural network
Deep learning
Efficient parsing
Medical image parsing
Issue Date: 2017
Publisher: Academic Press (an imprint of Elsevier)
Source: In SK Zhou, HK Greenspan & DG Shen (Eds.), Deep learning for medical image analysis, Chapter 6, p. 133-154. Amsterdam: Academic Press (an imprint of Elsevier) [2017] How to cite?
Abstract: With the development of deep learning techniques, the performance of object detection has been significantly advanced. Although various methods have been designed to detect landmarks for computer-aided diagnosis, how to efficiently and effectively leverage deep learning approaches to detect sparsely distributed objects, such as mitosis and cerebral microbleeds, from large scale medical images hasn't been fully explored. In this chapter, we introduce a two-stage cascaded deep learning framework, referred as deep cascaded networks, to detect sparsely distributed objects that provide clinical significance with both high efficiency and accuracy. Specifically, the first screening stage with coarse retrieval model rapidly retrieves potential candidates, and subsequently the second discrimination stage with the fine discrimination model focuses on those candidates to further accurately single out the true targets from challenging mimics. Furthermore, we corroborate the importance of volumetric feature representations for volumetric imaging modalities by exploiting 3D convolutional neural networks. Extensive experimental results on the challenging problems, including mitosis detection from 2D histopathological images and cerebral microbleed detection from 3D magnetic resonance images, demonstrated superior performance of our framework in terms of both speed and accuracy.
ISBN: 9780128104095
DOI: 10.1016/B978-0-12-810408-8.00008-0
Appears in Collections:Book Chapter

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