In healthcare, medical image segmentation is crucial for accurate disease diagnosis and the development of effective treatment strategies. Early detection can significantly aid in managing diseases and potentially prevent their progression. Machine learning, particularly deep convolutional neural networks, has emerged as a promising approach to addressing segmentation challenges. Traditional methods like U-Net use encoding blocks for local representation modeling and decoding blocks to uncover semantic relationships. However, these models often struggle with multi-scale objects exhibiting significant variations in texture and shape, and they frequently fail to capture long-range dependencies in the input data. Transformers designed for sequence-to-sequence predictions have been proposed as alternatives, utilizing global self-attention mechanisms. Yet, they can sometimes lack precise localization due to insufficient granular details. To overcome these limitations, we introduce TransDAE: a novel approach that reimagines the self-attention mechanism to include both spatial and channel-wise associations across the entire feature space, while maintaining computational efficiency. Additionally, TransDAE enhances the skip connection pathway with an inter-scale interaction module, promoting feature reuse and improving localization accuracy. Remarkably, TransDAE outperforms existing state-of-the-art methods on the Synaps multi-organ dataset, even without relying on pre-trained weights.
HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation
Iran University of Science and Technology（伊朗科技大学）；RWTH Aachen University（亚琛工业大学）；Shahid Beheshti University（沙希德·贝赫什提大学）；University of Montreal（蒙特利尔大学）；Polytechnique Montreal（蒙特利尔理工学院）；Quebec AI Institute（魁北克人工智能研究所）；University of Regensburg（雷根斯堡大学）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）
MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation
Yale University（耶鲁大学）；University of Florida（佛罗里达大学）；Wichita State University（威奇托州立大学）；Purdue University（普渡大学）；University of California, Berkeley（加利福尼亚大学伯克利分校）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Stevens Institute of Technology（史蒂文斯理工学院）；Zhejiang University（浙江大学）
Hierarchical Attention Networks for Medical Image Segmentation
Renmin University of China（中国人民大学）；AInnovation Technology Co., Ltd.（创新奇智科技有限公司）；Northwestern Polytechnical University（西北工业大学）；Visionary Intelligence Ltd.（远景智能有限公司）；Beijing Tongren Hospital（北京同仁医院）；Peking Union Medical College Hospital（北京协和医院）
Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding
East China Normal University（华东师范大学）；Stanford University（斯坦福大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Hong Kong（香港大学）；University of Florida（佛罗里达大学）；UC Santa Cruz（加州大学圣克鲁兹分校）
Mamba Goes HoME: Hierarchical Soft Mixture-of-Experts for 3D Medical Image Segmentation
Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw（华沙大学数学、信息学与力学系）；Faculty of Mathematics and Computer Science, Jagiellonian University（雅盖隆大学数学与计算机科学系）；Institute of AI for Health, Helmholtz Munich（海德堡医学院人工智能与健康研究所）；Faculty of Electronics and Information Technology, Warsaw University of Technology（华沙理工大学电子与信息技术系）；NASK - National Research Institute（国家研究 institute）；Faculty of Radiology, Massachusetts General Hospital（麻省总医院放射学系）；Department of Radiology, Harvard Medical School（哈佛医学院放射学系）
Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework
State Key Laboratory of Multimodal Artificial Intelligence System, Institute of Automation, Chinese Academy of Science, China（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院，中国）；School of Artificial Intelligence, University of Chinese Academy of Science, China（人工智能学院，中国科学院大学，中国）；Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing（放射科，北医三院，北京医科大学，中国医学科学院，北京）
Scribble-Supervised Medical Image Segmentation with Dynamic Teacher Switching and Hierarchical Consistency
1 Carnegie Mellon University, PA 15213, United States 2 PASSIO Lab, North Carolina A\&T State University, NC 27401, United States 3 University of Houston, TX 77004, United States 4 Northwestern University, IL 60611, United States
UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；Siemens Healthineers（西门子医疗）；Annalise-AI, Pty, Ltd（Annalise人工智能私人有限公司）；Google Cloud AI（谷歌云人工智能）
RF-HiT: Rectified Flow Hierarchical Transformer for General Medical Image Segmentation
Computer Science department Mohamed Khider University Biskra, Algeria；ISASI, CNR \& University of Salento 73100 Lecce, Italy；AI University of the Basque Country (UPV/EHU), Spain；Sorbonne Center for Artificial Intelligence, Sorbonne University Abu Dhabi, UAE
Dual-Adaptive SAM3: Hierarchical Routing over Low-Rank Expert Layers for Parameter-Efficient Medical Image Segmentation
Shenzhen Research Institute, The Chinese University of Hong Kong（香港中文大学深圳研究院）；University of Science and Technology of China（中国科学技术大学）；Nanyang Technological University（南洋理工大学）；Imperial Global Singapore (IGS), Imperial College London（伦敦帝国理工学院新加坡帝国全球（IGS））
Hierarchical Modeling for Medical Visual Question Answering with Cross-Attention Fusion
School of Artificial Intelligence, Tiangong University（天津工业大学人工智能学院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Linying Medical Technology (Shenzhen) Co., Ltd.（临影医疗科技（深圳）有限公司）
Hierarchical Vision-Language Learning for Medical Out-of-Distribution Detection
Peng Cheng Laboratory（鹏城实验室）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Nottingham Malaysia（诺丁汉大学（马来西亚））；Key Laboratory of Machine Intelligence and Advanced Computing, MOE（机器智能与高级计算重点实验室）
MedPruner: Training-Free Hierarchical Token Pruning for Efficient 3D Medical Image Understanding in Vision-Language Models
Chinese University of Hong Kong（香港中文大学）；Westlake University（西湖大学）；Southern Medical University（南方医科大学）；Jiangnan University（江南大学）；Dalian University of Technology（大连理工大学）；Shanghai Jiao Tong University（上海交通大学）；University of Copenhagen（哥本哈根大学）
DeepTumorVQA: A Hierarchical 3D CT Benchmark for Stage-Wise Evaluation of Medical VLMs and Tool-Augmented Agents
Johns Hopkins University（约翰霍普金斯大学）；University of Bologna（博洛尼亚大学）；Istanbul Medipol University（伊斯坦布尔梅迪波尔大学）；Center for Biomolecular Nanotechnologies, Istituto Italiano di Tecnologia（生物分子纳米技术中心，意大利技术研究院）；The First Affiliated Hospital, Sun Yat-Sen University（中山大学第一附属医院）；Tongji University（同济大学）
HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach
University of Virginia（弗吉尼亚大学）；University of California, Los Angeles (UCLA)（加利福尼亚大学洛杉矶分校）；The Aga Khan University（阿迦汗大学）；University of Zambia School of Medicine（赞比亚大学医学院）；Queen Mary University of London（伦敦玛丽女王大学）
SHAPE: Structure-aware Hierarchical Unsupervised Domain Adaptation with Plausibility Evaluation for Medical Image Segmentation
School of Software, Northwestern Polytechnical University（西北工业大学软件学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；School of Software Engineering, USTC（USTC软件工程学院）；Australian Institute of Health Innovation, Macquarie University（麦考瑞大学健康创新研究所）；Faculty of Applied Science, Macao Polytechnic University（澳门理工学院应用科学学院）；School of Computer Software, Tianjin University（天津大学计算机软件学院）
UniH$^3$: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration
School of Biological Science and Medical Engineering, Beihang University（北京航空航天大学生物科学与医学工程学院）；Department of Biomedical Engineering, Tsinghua University（清华大学生物医学工程系）
Hi-End-MAE: Hierarchical encoder-driven masked autoencoders are stronger vision learners for medical image segmentation
University of Science and Technology of China (USTC)（中国科学技术大学）；Suzhou Institute for Advanced Research, USTC（中国科学技术大学苏州高等研究院）；Stanford University（斯坦福大学）；State Key Laboratory of Precision and Intelligent Chemistry, USTC（中国科学技术大学精准与智能化学国家重点实验室）；Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS（中国科学院计算技术研究所中国科学院智能信息处理重点实验室）
Imperial College London（帝国理工学院）；École des Ponts（巴黎路桥学院）；EDF R&D（法国电力公司研发中心）；Royal Brompton and Harefield Hospital（皇家布朗普顿和 Harefield 医院）；Tongji University（同济大学）
Hierarchical Feature Learning for Medical Point Clouds via State Space Model
Shenzhen Key Laboratory of Ubiquitous Data Enabling, Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院深圳市普适数据赋能重点实验室）；Pengcheng Laboratory（鹏城实验室）
