Breast cancer lesion segmentation in DCE-MRI remains challenging due to heterogeneous tumor morphology and indistinct boundaries. To address these challenges, this study proposes a novel hybrid segmentation network, HCMA-UNet, for lesion segmentation of breast cancer. Our network consists of a lightweight CNN backbone and a Multi-view Axial Self-Attention Mamba (MISM) module. The MISM module integrates Visual State Space Block (VSSB) and Axial Self-Attention (ASA) mechanism, effectively reducing parameters through Asymmetric Split Channel (ASC) strategy to achieve efficient tri-directional feature extraction. Our lightweight model achieves superior performance with 2.87M parameters and 126.44 GFLOPs. A Feature-guided Region-aware loss function (FRLoss) is proposed to enhance segmentation accuracy. Extensive experiments on one private and two public DCE-MRI breast cancer datasets demonstrate that our approach achieves state-of-the-art performance while maintaining computational efficiency. FRLoss also exhibits good cross-architecture generalization capabilities. The source code is available at https://github.com/Haoxuanli-Thu/HCMA-UNet.
LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation
School of Computer Science, Peking University（北京大学计算机学院）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；Key Laboratory of High Confidence Software Technologies, Ministry of Education（教育部高可信软件技术重点实验室）；National Engineering Research Center for Software Engineering, Peking University（北京大学国家软件工程研究中心）
VM-UNET-V2 Rethinking Vision Mamba UNet for Medical Image Segmentation
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；Huazhong University of Science and Technology（华中科技大学）；Jiangsu Province Hospital of Chinese Medicine（江苏省中医院）
VM-UNet: Vision Mamba UNet for Medical Image Segmentation
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）
Topo-VM-UNetV2: Encoding Topology into Vision Mamba UNet for Polyp Segmentation
Department of Computer Science, University of Texas Rio Grande Valley（德克萨斯理工大学里奥格兰德分校计算机科学系）；Department of Mathematical Sciences, The University of Texas at Dallas（德克萨斯大学达拉斯分校数学科学系）
xLSTM-UNet can be an Effective 2D & 3D Medical Image Segmentation Backbone with Vision-LSTM (ViL) better than its Mamba Counterpart
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；KOKONI, Moxin (Huzhou) Tech. Co., LTD.（墨芯（湖州）科技有限公司KOKONI）；School of Information Engineering, Huzhou University（湖州师范学院信息工程学院）；Information Systems Technology and Design Pillar, Singapore University of Technology and Design（新加坡科技设计大学信息系统技术与设计 pillar）；School of Information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学技术学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）
Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院保罗·C·劳特伯生物医学成像研究中心）；Peng Cheng Laboratory（鹏城实验室）；University of Chinese Academy of Sciences（中国科学院大学）；The University of Hong Kong（香港大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
UNet with Self-Adaptive Mamba-Like Attention and Causal-Resonance Learning for Medical Image Segmentation
Faculty of Computing and IT (FCIT)（计算与信息技术学院）；Sohar University（索哈尔大学）；Department of Intelligent Systems（智能系统系）；KTH Royal Institute of Technology（皇家理工学院）；College of Computer and Information Sciences（计算机与信息科学学院）；Imam Mohammad Ibn Saud Islamic University (IMSIU)（伊玛目穆罕默德·本·沙特伊斯兰大学）；University of Auckland（奥克兰大学）；Department of Computer Science, Faculty of Science（科学学院计算机科学系）；Northern Border University（北方边境大学）
Topology-Aware Wavelet Mamba for Airway Structure Segmentation in Postoperative Recurrent Nasopharyngeal Carcinoma CT Scans
School of Software Engineering, Sun Yat-sen University（中山大学软件工程学院）；School of Microelectronics, Shanghai University（上海大学微电子学院）；Electronic and Computer Engineering, The Hong Kong University of Science and Technology（香港科技大学电子与计算机工程系）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；Eye & ENT Hospital, Fudan University（复旦大学附属眼耳鼻喉科医院）
School of Advanced Technology, Xi’an Jiaotong-Liverpool University（西交利物浦大学先进技术学院）；School of Electronics and Information Engineering, Beihang University（北京航空航天大学电子信息工程学院）；Cambridge University（剑桥大学）；Beijing Shijitan Hospital, Capital Medical University（首都医科大学附属北京世纪坛医院）；University of Liverpool（利物浦大学）
HWA-UNETR: Hierarchical Window Aggregate UNETR for 3D Multimodal Gastric Lesion Segmentation
School of Computer Science and Engineering（计算机科学与工程学院）；School of Future Technology（未来技术学院）；South China University of Technology（华南理工大学）；Department of Medical Imaging（医学影像科）；Guangzhou Institute of Cancer Research（广州癌症研究所）；the Affiliated Cancer Hospital（附属肿瘤医院）；Guangzhou Medical University（广州医学院）；Department of Radiology（放射科）；Meizhou People’s Hospital（梅州市人民医院）；School of Computer Science and Technology（计算机科学与技术学院）；Hainan University（海南大学）；Department of Oncology Institute（肿瘤研究所）
EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis
School of Computer Technology and Application, Qinghai University（青海大学计算机技术与应用学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Intelligent Computing and Application Laboratory of Qinghai Province, Qinghai University（青海省智能计算与应用实验室）；School of Chemical Engineering, Qinghai University（青海大学化学工程学院）；Department of General Surgery, Affiliated Hospital of Qinghai University（青海大学附属医院普外科）；Department of Medical Engineering Integration and Translational Application, Affiliated Hospital of Qinghai University（青海大学附属医院医学工程整合与转化应用系）
