Contrastive learning, which aims to capture general representation from unlabeled images to initialize the medical analysis models, has been proven effective in alleviating the high demand for expensive annotations. Current methods mainly focus on instance-wise comparisons to learn the global discriminative features, however, pretermitting the local details to distinguish tiny anatomical structures, lesions, and tissues. To address this challenge, in this paper, we propose a general unsupervised representation learning framework, named local discrimination (LD), to learn local discriminative features for medical images by closely embedding semantically similar pixels and identifying regions of similar structures across different images. Specifically, this model is equipped with an embedding module for pixel-wise embedding and a clustering module for generating segmentation. And these two modules are unified through optimizing our novel region discrimination loss function in a mutually beneficial mechanism, which enables our model to reflect structure information as well as measure pixel-wise and region-wise similarity. Furthermore, based on LD, we propose a center-sensitive one-shot landmark localization algorithm and a shape-guided cross-modality segmentation model to foster the generalizability of our model. When transferred to downstream tasks, the learned representation by our method shows a better generalization, outperforming representation from 18 state-of-the-art (SOTA) methods and winning 9 out of all 12 downstream tasks. Especially for the challenging lesion segmentation tasks, the proposed method achieves significantly better performances. The source codes are publicly available at https://github.com/HuaiChen-1994/LDLearning.
Unsupervised Task Design to Meta-Train Medical Image Classifiers
Australian Institute for Machine Learning, The University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；Institute for Systems and Robotics, Instituto Superior Tecnico（高等技术学院系统与机器人研究所）
Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis
School of Computer Science, University of Sydney（悉尼大学计算机科学学院）；Royal Prince Alfred Hospital（皇家阿尔弗雷德王子医院）；Sydney Medical School, University of Sydney（悉尼大学悉尼医学院）；Med-X Research Institute, Shanghai Jiao Tong University（上海交通大学Med-X研究院）
LLaMA-Reg: Using LLaMA 2 for Unsupervised Medical Image Registration
Faculty of Information Science Engineering and Automation, Kunming University of Science and Technology（昆明理工大学信息科学工程与自动化学院）；Taizhou Hospital of Zhejiang Province Affiliated with Wenzhou Medical University（温州医科大学附属浙江省台州医院）；Enze Hospital, Taizhou Enze Medical Centre (Group)（台州恩泽医疗中心（集团）恩泽医院）；The Second Affiliated Hospital of Dalian Medical University（大连医科大学附属第二医院）
Unsupervised deep clustering for predictive texture pattern discovery in medical images
Computational Imaging Research Lab (CIR)（计算成像研究实验室（CIR））；Department of Biomedical Imaging and Image-guided Therapy（生物医学成像与影像引导治疗系）；Medical University of Vienna（维也纳医科大学）
Low-Rank Velocity Fields as a Structural Prior for Unsupervised 4D Medical Image Interpolation
Southern University of Science and Technology（南方科技大学）；Shenzhen University of Advanced Technology（深圳先进技术研究院）；Shenzhen University Medical School（深圳大学医学院）；Shenzhen University（深圳大学）；Research Institute of Trustworthy Autonomous Systems（可信自主系统研究院）
Unsupervised Deformable Medical Image Registration via Pyramidal Residual Deformation Fields Estimation
Southern Medical University（南方医科大学）；Shenzhen University（深圳大学）
形变场估计是医学图像配准中一个重要而又具有挑战性的问题。近年来，深度学习技术已成为简化配准问题的一种很有前途的方法，并逐渐应用于医学图像配准。但是，现有的大多数深度学习配准都没有考虑到当感受野不能覆盖运动图像和固定图像中的相应特征时，无法输出准确的位移值的问题。事实上，由于感受野的限制，3 x 3内核在高/原始分辨率下难以覆盖相应的特征。多分辨率和多卷积技术可以改善但无法避免这个问题。在这项研究中，我们在移动和固定图像上构建了金字塔特征集，并使用扭曲的移动和固定特征来估计每个尺度下的“残余”变形场，称为金字塔残余变形场估计模块（PRDFE模块）。该方法通过上采样和加权求和各尺度下的"残余"形变场，计算出各尺度下的"总"形变场，有效准确地将形变场从低分辨率转换到高分辨率，并用于各尺度下运动特征的变形。仿真和真实脑数据实验结果表明，该方法提高了配准的准确性和形变场的合理性。
HiFi-Syn: Hierarchical Granularity Discrimination for High-Fidelity Synthesis of MR Images with Structure Preservation
Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院健康科学技术学院）；Clinical Neuroscience Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院临床神经科学中心）；National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, Shanghai Jiao Tong University（上海交通大学先进磁共振诊断与治疗技术国家工程研究中心）；Ping An Technology (Shenzhen) Co., Ltd.（平安科技（深圳）有限公司）
M-IDoL: Information Decomposition for Modality-Specific and Diverse Representation Learning in Medical Foundation Model
School of Computer Science and Technique（计算机科学与技术学院）；Tongji University（同济大学）；School of Communications and Electronic Engineering（通讯与电子工程学院）；East China Normal University（华东师范大学）
Unsupervised Multimodal 3D Medical Image Registration with Multilevel Correlation Balanced Optimization
College of Electrical and Information Engineering, Hunan University（湖南大学电气与信息工程学院）；National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（湖南大学机器人视觉感知与控制技术国家工程研究中心）；Cornell University（康奈尔大学）
Latent Diffusion Autoencoders: Toward Efficient and Meaningful Unsupervised Representation Learning in Medical Imaging
Department of Electrical and Information Engineering (DIEI), University of Cassino and Southern Lazio（电气与信息工程系（DIEI），卡斯诺和南部拉齐亚大学）；Diagnostic Image Analysis Group, Radboud University Medical Center（影像诊断分析组，拉德堡德大学医学中心）；Department of Medical Imaging, Radboud University Medical Center（医学成像系，拉德堡德大学医学中心）
Distribution Aligned Diffusion and Prototype-guided network for Unsupervised Domain Adaptive Segmentation
Xi’an Jiaotong University（西安交通大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of California, Santa Cruz（加州大学圣克鲁兹分校）
Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images
Australian Institute for Machine Learning, University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；South Australian Health and Medical Research Institute（南澳大利亚健康与医学研究所）；Faculty of Health and Medical Sciences, University of Adelaide（阿德莱德大学健康与医学科学学院）
Building RadiologyNET: Unsupervised annotation of a large-scale multimodal medical database
University of Rijeka – Faculty of Engineering（里耶卡大学工程学院）；Center for Artificial Intelligence and Cybersecurity（人工智能与网络安全中心）；Medical University of Graz（格拉茨医科大学）
Medical Image Segmentation via Unsupervised Convolutional Neural Network
Johns Hopkins Medical Institutes（约翰斯·霍普金斯医学院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Department of Radiology and Radiological Science（放射学与放射科学系）；Department of Electrical and Computer Engineering（电气与计算机工程系）
Towards Universal Unsupervised Anomaly Detection in Medical Imaging
Technical University of Munich（慕尼黑工业大学）；Helmholtz AI and Helmholtz Center Munich（亥姆霍兹人工智能与亥姆霍兹慕尼黑中心）；Klinikum Rechts der Isar（伊萨尔河右岸医院）；Imperial College London（伦敦帝国理工学院）
COSMOS: Cross-Modality Unsupervised Domain Adaptation for 3D Medical Image Segmentation based on Target-aware Domain Translation and Iterative Self-Training
Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Medical Images
Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany（技术大学慕尼黑计算机辅助医疗程序）；Munich Center of Machine Learning, Munich, Germany（慕尼黑机器学习中心）；Department for Vascular and Endovascular Surgery, rechts der Isar University Hospital, Technical University of Munich, Munich, Germany（技术大学慕尼黑血管和内血管外科部门）
TransMorph: Transformer for unsupervised medical image registration
Johns Hopkins Medical Institutes（约翰·霍普金斯医学院）；Johns Hopkins University（约翰·霍普金斯大学）；NVIDIA Corporation（英伟达公司）；Duke University Medical Center（杜克大学医学中心）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
