TY - GEN
T1 - Learn to Step-wise Focus on Targets for Biomedical Image Segmentation
AU - Wei, Siyuan
AU - Wang, Li
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Current segmentation networks based on the encoder-decoder architecture have tried recovering spatial information by stacking convolution blocks in the decoder. Unconventionally, we consider that iteratively exploiting spatial attention from high stage to refine lower stage features can form an attention-driven mechanism to step-wise recover detailed features. In this paper, we rethink image segmentation from a novel perspective: a process of step-wise focusing on targets. We develop a lightweight Focus Module (FM) and present a powerful transplantable Step-wise Focus Network (SFN) for biomedical image segmentation. FM extracts high-level spatial attention and combines it with low-level features by our proposed focus learning to generate revised features. Our SFN extends U-Net encoder sub-network and employs just FMs to construct a focus path in order to consistently refine features. We evaluate SFNs in comparison with U-Net and other state-of-art methods on multiple biomedical image segmentation benchmarks. While using 30% floating-point operations and 60% parameters of U-Net, SFNs achieve great performances without any postprocessing.
AB - Current segmentation networks based on the encoder-decoder architecture have tried recovering spatial information by stacking convolution blocks in the decoder. Unconventionally, we consider that iteratively exploiting spatial attention from high stage to refine lower stage features can form an attention-driven mechanism to step-wise recover detailed features. In this paper, we rethink image segmentation from a novel perspective: a process of step-wise focusing on targets. We develop a lightweight Focus Module (FM) and present a powerful transplantable Step-wise Focus Network (SFN) for biomedical image segmentation. FM extracts high-level spatial attention and combines it with low-level features by our proposed focus learning to generate revised features. Our SFN extends U-Net encoder sub-network and employs just FMs to construct a focus path in order to consistently refine features. We evaluate SFNs in comparison with U-Net and other state-of-art methods on multiple biomedical image segmentation benchmarks. While using 30% floating-point operations and 60% parameters of U-Net, SFNs achieve great performances without any postprocessing.
UR - https://www.scopus.com/pages/publications/85075667274
U2 - 10.1007/978-3-030-32692-0_60
DO - 10.1007/978-3-030-32692-0_60
M3 - 会议稿件
AN - SCOPUS:85075667274
SN - 9783030326913
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 525
EP - 532
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 -