AGNet: Attention-Guided Network for Surgical Tool Presence Detection
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摘要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.
出版社接受日期10.07.2017
著者Xiaowei Hu, Lequan Yu, Hao Chen, Jing Qin, and Pheng-Ann Heng
會議名稱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
會議開始日14.09.2017
會議完結日14.09.2017
會議地點Quebec
會議國家/地區加拿大
會議論文集題名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
出版年份2017
卷號10553
出版社Springer
頁次186 - 194
國際標準書號978-3-319-67557-2
國際標準期刊號0302-9743
語言美式英語

上次更新時間 2020-23-11 於 01:48