Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74346
Title: AGNet : attention-guided network for surgical tool presence detection
Authors: Hu, X
Yu, L
Chen, H
Qin, J 
Heng, PA
Keywords: Attention-guided network
Cholecystectomy
Deep learning
Laparoscopic videos
Surgical tool recognition
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. 10553, p. 186-194 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: We propose a novel approach to automatically recognize the presence of surgical tools in surgical videos, which is quite challenging due to the large variation and partially appearance of surgical tools, the complicated surgical scenes, and the co-occurrence of some tools in the same frame. Inspired by human visual attention mechanism, which first orients and selects some important visual cues and then carefully analyzes these focuses of attention, we propose to first leverage a global prediction network to obtain a set of visual attention maps and a global prediction for each tool, and then harness a local prediction network to predict the presence of tools based on these attention maps. We apply a gate function to obtain the final prediction results by balancing the global and the local predictions. The proposed attention-guided network (AGNet) achieves state-of-the-art performance on m2cai16-tool dataset and surpasses the winner in 2016 by a significant margin.
Description: 3rd International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017 and 7th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017, 14 - 14 September 2017
URI: http://hdl.handle.net/10397/74346
ISBN: 9783319675572
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-319-67558-9_22
Appears in Collections:Conference Paper

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