Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Deep reinforcement learning for active breast lesion detection from DCE-MRI|
|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|
|Series/Report no.:||Lecture Notes in Computer Science; 10435|
|Conference Name:||International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) (11 Sep 2017 - 13 Sep 2017 : Quebec City, Canada)|
|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|
|Appears in Collections:||Computer Science publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.