A large labeled dataset is a key to the success of supervised deep learning, but for medical image segmentation, it is highly challenging to obtain sufficient annotated images for model training. In many scenarios, unannotated images are abundant and easy to acquire. Self-supervised learning (SSL) has shown great potentials in exploiting raw data information and representation learning. In this paper, we propose Hierarchical Self-Supervised Learning (HSSL), a new self-supervised framework that boosts medical image segmentation by making good use of unannotated data. Unlike the current literature on task-specific self-supervised pretraining followed by supervised fine-tuning, we utilize SSL to learn task-agnostic knowledge from heterogeneous data for various medical image segmentation tasks. Specifically, we first aggregate a dataset from several medical challenges, then pre-train the network in a self-supervised manner, and finally fine-tune on labeled data. We develop a new loss function by combining contrastive loss and classification loss and pretrain an encoder-decoder architecture for segmentation tasks. Our extensive experiments show that multi-domain joint pre-training benefits downstream segmentation tasks and outperforms single-domain pre-training significantly. Compared to learning from scratch, our new method yields better performance on various tasks (e.g., +0.69% to +18.60% in Dice scores with 5% of annotated data). With limited amounts of training data, our method can substantially bridge the performance gap w.r.t. denser annotations (e.g., 10% vs.~100% of annotated data).
UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation
The University of Hong Kong（香港大学）；SenseTime Research（商汤科技研究院）；Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen（香港中文大学深圳大数据研究院）
Comparative Benchmarking of Failure Detection Methods in Medical Image Segmentation: Unveiling the Role of Confidence Aggregation
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Medical Faculty Heidelberg, Heidelberg University（海德堡大学海德堡医学院）；Helmholtz Imaging（亥姆霍兹成像）；Heidelberg University Hospital（海德堡大学医院）
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）
School of Artificial Intelligence and School of Automation, Xi’an University of Posts and Telecommunications（人工智能学院和自动化学院，西安邮电大学）；Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University（电气电子工程系，香港理工大学）
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）
DCELANM-Net:Medical Image Segmentation based on Dual Channel Efficient Layer Aggregation Network with Learner
School of Computer Science, Hubei University of Technology（湖北工业大学计算机学院）；Lublin University of Technology（卢布林工业大学）；Lviv Polytechnic National University（利沃夫国立理工大学）
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）
FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentation
Southern University of Science and Technology (SUSTech)（南方科技大学）；Jiaxing Research Institute, Southern University of Science and Technology（南方科技大学嘉兴研究院）；the University of Hong Kong（香港大学）；the Hong Kong University of Science and Technology（香港科技大学）
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
Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation
Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）；Jiaxing Research Institute, Southern University of Science and Technology（南方科技大学嘉兴研究院）
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）
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）
Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation
Johns Hopkins University（约翰斯·霍普金斯大学）；NVIDIA（英伟达）；Japan Self-Defense Forces Central Hospital（日本自卫队中央医院）；Xiangyang NO.1 People Hospital（襄阳市第一人民医院）；National Cancer Institute（美国国家癌症研究所）；National Institutes of Health（美国国立卫生研究院）；ASST Santi Paolo e Carlo（圣保罗与卡洛医院）；University of Milan（米兰大学）；Foundation IRCCS Ospedale Maggiore Policlinico Hospital（IRCCS马焦雷综合医院基金会）
Hierarchical Spatial and Channel Aggregation for Cross-domain Few-shot Segmentation
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；State Key Laboratory of Intelligent Manufacturing of Advanced Construction Machinery（先进工程机械智能制造国家重点实验室）
Decouple, Purify and Unite: Semantic-Structural Prototype Learning for Federated Medical Segmentation
Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（北京协和医院，中国医学科学院北京协和医学院）；Beijing University of Posts and Telecommunications（北京邮电大学）；Nanyang Technological University（南洋理工大学）；Ant Group（蚂蚁集团）；Shanghai Innovation Institute（上海创新研究院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；DAMO Academy, Alibaba Group（阿里巴巴达摩院）；Fourth Military Medical University（中国人民解放军空军军医大学）；Macao Polytechnic University（澳门理工大学）；Wuhan University（武汉大学）；The Hong Kong Polytechnic University（香港理工大学）
PRIMA: Pre-Training with Risk-Integrated Image--Metadata Alignment for Medical Diagnosis with LLM-Based Feature Aggregation
Department of Biomedical Engineering, Duke University（杜克大学生物医学工程系）；Department of Ophthalmology and Visual Sciences, University of Michigan（密歇根大学眼科与视觉科学系）
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）
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）
HOLA: Enhancing Audio-visual Deepfake Detection via Hierarchical Contextual Aggregations and Efficient Pre-training
Xi'an Jiaotong University（西安交通大学）；Zhengzhou University（郑州大学）；University of Science and Technology of China（中国科学技术大学）；Dalian University of Technology（大连理工大学）；Zhejiang Lab（浙江实验室）
