TY - GEN
T1 - Lesion Detection by Efficiently Bridging 3D Context
AU - Zhang, Zhishuai
AU - Zhou, Yuyin
AU - Shen, Wei
AU - Fishman, Elliot
AU - Yuille, Alan
N1 - Funding Information:
Acknowledgements. This work was supported by the Lustgarten Foundation for Pancreatic Cancer Research and NSFC No. 61672336.
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Lesion detection in CT (computed tomography) scan images is an important yet challenging task due to the low contrast of soft tissues and similar appearance between lesion and the background. Exploiting 3D context information has been studied extensively to improve detection accuracy. However, previous methods either use a 3D CNN which usually requires a sliding window strategy to inference and only acts on local patches; or simply concatenate feature maps of independent 2D CNNs to obtain 3D context information, which is less effective to capture 3D knowledge. To address these issues, we design a hybrid detector to combine benefits from both of the above methods. We propose to build several light-weighted 3D CNNs as subnets to bridge 2D CNNs’ intermediate features, so that 2D CNNs are connected with each other which interchange 3D context information while feed-forwarding. Comprehensive experiments in DeepLesion dataset show that our method can combine 3D knowledge effectively and provide higher quality backbone features. Our detector surpasses the current state-of-the-art by a large margin with comparable speed and GPU memory consumption.
AB - Lesion detection in CT (computed tomography) scan images is an important yet challenging task due to the low contrast of soft tissues and similar appearance between lesion and the background. Exploiting 3D context information has been studied extensively to improve detection accuracy. However, previous methods either use a 3D CNN which usually requires a sliding window strategy to inference and only acts on local patches; or simply concatenate feature maps of independent 2D CNNs to obtain 3D context information, which is less effective to capture 3D knowledge. To address these issues, we design a hybrid detector to combine benefits from both of the above methods. We propose to build several light-weighted 3D CNNs as subnets to bridge 2D CNNs’ intermediate features, so that 2D CNNs are connected with each other which interchange 3D context information while feed-forwarding. Comprehensive experiments in DeepLesion dataset show that our method can combine 3D knowledge effectively and provide higher quality backbone features. Our detector surpasses the current state-of-the-art by a large margin with comparable speed and GPU memory consumption.
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U2 - 10.1007/978-3-030-32692-0_54
DO - 10.1007/978-3-030-32692-0_54
M3 - Conference contribution
AN - SCOPUS:85075644561
SN - 9783030326913
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 470
EP - 478
BT - Machine Learning in Medical Imaging - 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Proceedings
A2 - Suk, Heung-Il
A2 - Liu, Mingxia
A2 - Lian, Chunfeng
A2 - Yan, Pingkun
PB - Springer
T2 - 10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 13 October 2019
ER -