Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74347
Title: Automated pulmonary nodule detection via 3D convnets with online sample filtering and hybrid-loss residual learning
Authors: Dou, Q
Chen, H
Jin, Y
Lin, H
Qin, J 
Heng, PA
Issue Date: 2017
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2017, v. 10435, p. 630-638 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: In this paper, we propose a novel framework with 3D convolutional networks (ConvNets) for automated detection of pulmonary nodules from low-dose CT scans, which is a challenging yet crucial task for lung cancer early diagnosis and treatment. Different from previous standard ConvNets, we try to tackle the severe hard/easy sample imbalance problem in medical datasets and explore the benefits of localized annotations to regularize the learning, and hence boost the performance of ConvNets to achieve more accurate detections. Our proposed framework consists of two stages: (1) candidate screening, and (2) false positive reduction. In the first stage, we establish a 3D fully convolutional network, effectively trained with an online sample filtering scheme, to sensitively and rapidly screen the nodule candidates. In the second stage, we design a hybrid-loss residual network which harnesses the location and size information as important cues to guide the nodule recognition procedure. Experimental results on the public large-scale LUNA16 dataset demonstrate superior performance of our proposed method compared with state-of-the-art approaches for the pulmonary nodule detection task.
Description: 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, 11 - 13 September 2017
URI: http://hdl.handle.net/10397/74347
ISBN: 9783319661780
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-319-66179-7_72
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