Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/118613
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Type: Conference paper
Title: Deep reinforcement learning for active breast lesion detection from DCE-MRI
Author: Maicas, G.
Carneiro, G.
Bradley, A.
Nascimento, J.
Reid, I.
Citation: Medical Image Computing and Computer Assisted Intervention – MICCAI 2017: 20th International Conference. Proceedings, Part III, 2017 / Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D., Duchesne, S. (ed./s), vol.10435 LNCS, pp.665-673
Publisher: Springer
Issue Date: 2017
Series/Report no.: Lecture Notes in Computer Science; 10435
ISBN: 9783319661780
ISSN: 0302-9743
1611-3349
Conference Name: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) (11 Sep 2017 - 13 Sep 2017 : Quebec City, Canada)
Statement of
Responsibility: 
Gabriel Maicas, Gustavo Carneiro, Andrew P. Bradley, Jacinto C. Nascimento, Ian Reid
Abstract: We present a novel methodology for the automated detection of breast lesions from dynamic contrast-enhanced magnetic resonance volumes (DCE-MRI). Our method, based on deep reinforcement learning, significantly reduces the inference time for lesion detection compared to an exhaustive search, while retaining state-of-art accuracy. This speed-up is achieved via an attention mechanism that progressively focuses the search for a lesion (or lesions) on the appropriate region(s) of the input volume. The attention mechanism is implemented by training an artificial agent to learn a search policy, which is then exploited during inference. Specifically, we extend the deep Q-network approach, previously demonstrated on simpler problems such as anatomical landmark detection, in order to detect lesions that have a significant variation in shape, appearance, location and size. We demonstrate our results on a dataset containing 117 DCE-MRI volumes, validating run-time and accuracy of lesion detection.
Rights: © Springer International Publishing AG 2017
RMID: 0030075999
DOI: 10.1007/978-3-319-66179-7_76
Grant ID: http://purl.org/au-research/grants/arc/DP140102794
http://purl.org/au-research/grants/arc/CE140100016
http://purl.org/au-research/grants/arc/FL130100102
Appears in Collections:Computer Science publications

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