High-Order Interaction and Low-Order Paralleliza-tion of Features Fusion With Novel Mamba-UNet Architecture for Medical Image SegmentationOA
High-Order Interaction and Low-Order Paralleliza-tion of Features Fusion With Novel Mamba-UNet Architecture for Medical Image Segmentation
Qianhang Du;Zhenyu Lei;Jiujun Cheng;Masaaki Omura;Hideyuki Hasegawa;Shangce Gao
Faculty of Engineering,University of Toyama,Toyama-shi 930-8555,JapanFaculty of Engineering,University of Toyama,Toyama-shi 930-8555,JapanSchool of Electronics and Information,Tongji University,Shanghai 200092,China||Key Laboratory of Embedded System and Service Computing,Ministry of Education,Tongji University,Shanghai 200092,ChinaFaculty of Engineering,University of Toyama,Toyama-shi 930-8555,JapanFaculty of Engineering,University of Toyama,Toyama-shi 930-8555,JapanFaculty of Engineering,University of Toyama,Toyama-shi 930-8555,Japan
2D-selective-scan(SS2D)deep learninghigh-low-order feature fusion(HLFF)Mambamedical image segmentationstate-space models
2D-selective-scan(SS2D)deep learninghigh-low-order feature fusion(HLFF)Mambamedical image segmentationstate-space models
《自动化学报（英文版）》 2026 (6)
1378-1391,14
This research was partially supported by the Japan Society for the Promotion of Science(JSPS)KAKENHI(JP25K21298,JP25K03179),and Japan Science and Technology Agency(JST)Support for Pioneering Research Initiated by the Next Generation(SPRING)(JPMJSP2145).
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