Medical image fusion integrates complementary information from multiple imaging modalities to improve clinical interpretation. However, existing deep learningbased methods, including recent spatial-frequency frameworks such as AdaFuse and ASFE-Fusion, often suffer from a fundamental trade-off between global statistical similaritymeasured by correlation coefficient (CC) and mutual information (MI)and local structural fidelity. This paper proposes W-DUALMINE, a reliability-weighted dual-expert fusion framework designed to explicitly resolve this trade-off through architectural constraints and a theoretically grounded loss design. The proposed method introduces dense reliability maps for adaptive modality weighting, a dual-expert fusion strategy combining a global-context spatial expert and a wavelet-domain frequency expert, and a soft gradient-based arbitration mechanism. Furthermore, we employ a residual-to-average fusion paradigm that guarantees the preservation of global correlation while enhancing local details. Extensive experiments on CT-MRI, PET-MRI, and SPECT-MRI datasets demonstrate that W-DUALMINE consistently outperforms AdaFuse and ASFE-Fusion in CC and MI metrics while
The University of Hong Kong（香港大学）；ShanghaiTech University（上海科技大学）；Deepwise AI Lab（深睿人工智能实验室）；Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳）深圳大数据研究院）
Preserving maximal information is one of principles of designing self-supervised learning methodologies. To reach this goal, contrastive learning adopts an implicit way which is contrasting image pairs. However, we believe it is not fully optimal to simply use the contrastive estimation for preservation. Moreover, it is necessary and complemental to introduce an explicit solution to preserve more information. From this perspective, we introduce Preservational Learning to reconstruct diverse image contexts in order to preserve more information in learned representations. Together with the contr…
Chest radiographs are used for the diagnosis of multiple critical illnesses (e.g., Pneumonia, heart failure, lung cancer), for this reason, systems for the automatic or semi-automatic analysis of these data are of particular interest. An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists, ultimately allowing for better medical care of lung-, heart- and chest-related conditions. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information that is typically lost in the down-sampling …
The University of Hong Kong（香港大学）；ShanghaiTech University（上海科技大学）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与成像工程技术研究中心）
Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views. However, the preserved high-level semantics do not contain enough local information, which is vital in medical image analysis (e.g., image-based diagnosis and tumor segmentation). To mitigate the locality problem of comparative SSL, we propose to incorporate the task of pixel restoration for explicitly encoding more pixel-level information into high-level semantics. We also ad…
Linear Algebraic Approaches to Neuroimaging Data Compression: A Comparative Analysis of Matrix and Tensor Decomposition Methods for High-Dimensional Medical Images
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）
When the Edit Changes the Patient: Measuring Identity Preservation in Counterfactual Retinal Images
Technical University of Munich（慕尼黑工业大学）；TUM University Hospital（慕尼黑工业大学医院）；Munich Center of Machine Learning (MCML)（慕尼黑机器学习中心）；Munich Data Science Institute (MDSI)（慕尼黑数据科学研究所）；Sapienza University of Rome（罗马第一大学）；Faculty of Medicine, University of Southampton（南安普顿大学医学院）；Moorfields Eye Hospital NHS Foundation Trust（摩尔菲尔兹眼科医院NHS信托基金）；Imperial College London（伦敦帝国学院）
CENet: Context Enhancement Network for Medical Image Segmentation
Faculty of Informatics and Data Science, University of Regensburg（信息学院与数据科学学院，雷根施塔特大学）；Department of Industrial and Systems Engineering, Tarbiat Modares University（工业与系统工程系，塔里克特莫达res大学）；Department of Radiology & Department of Medicine, University of British Columbia（放射科与医学系，不列颠哥伦比亚大学）；Fraunhofer Institute for Digital Medicine MEVIS（数字医学研究所MEVIS）
Isfahan University of Technology（伊斯法罕理工大学）；University of Queensland（昆士兰大学）；University of Michigan（密歇根大学）
By increasing the volume of telemedicine information, the need for medical image compression has become more important. In angiographic images, a small ratio of the entire image usually belongs to the vasculature that provides crucial information for diagnosis. Other parts of the image are diagnostically less important and can be compressed with higher compression ratio. However, the quality of those parts affect the visual perception of the image as well. Existing methods compress foreground and background of angiographic images using different techniques. In this paper we first utilize convo…
Quantitative Metrics for Benchmarking Medical Image Harmonization
Children’s National Hospital（儿童国家医院）；Children’s Hospital of Philadelphia（费城儿童医院）；School of Medicine and Health Sciences, George Washington University（乔治华盛顿大学医学与健康科学学院）
MedFLIP: Medical Vision-and-Language Self-supervised Fast Pre-Training with Masked Autoencoder
University of Copenhagen（哥本哈根大学）；Tsinghua University（清华大学）；Shanghai Medical Image Insights（上海医学影像洞察）；Harvard University（哈佛大学）；University of Waterloo（滑铁卢大学）
SMFusion: Semantic-Preserving Fusion of Multimodal Medical Images for Enhanced Clinical Diagnosis
College of Information and Intelligence, Hunan Agricultural University（湖南农业大学信息与智能学院）；College of Artificial Intelligence, Nankai University（南开大学人工智能学院）
Learning Robust Medical Image Segmentation from Multi-source Annotations
Tsinghua University（清华大学）；Hong Kong University of Science and Technology（香港科技大学）；Chinese Academy of Sciences（中国科学院）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深圳-香港协同创新研究院）
TGC-Net: A Structure-Aware and Semantically-Aligned Framework for Text-Guided Medical Image Segmentation
School of Computer Science, Wuhan University（武汉大学计算机学院）；Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science（同济大学同济医学学院骨科部，华中科技大学）
Enhancing Global Sensitivity and Uncertainty Quantification in Medical Image Reconstruction with Monte Carlo Arbitrary-Masked Mamba
Imperial College London（帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）；College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区生物医学工程学院）；Human Phenome Institute, Fudan University（复旦大学人类表型组研究院）；Inselspital, University of Bern（伯尔尼大学因塞尔医院）；Technical University of Munich（慕尼黑工业大学）
Video is an essential imaging modality for diagnostics, e.g. in ultrasound imaging, for endoscopy, or movement assessment. However, video hasn't received a lot of attention in the medical image analysis community. In the clinical practice, it is challenging to utilise raw diagnostic video data efficiently as video data takes a long time to process, annotate or audit. In this paper we introduce a novel, fully automatic video summarization method that is tailored to the needs of medical video data. Our approach is framed as reinforcement learning problem and produces agents focusing on the prese…
Dynamic Perturbation-Adaptive Adversarial Training on Medical Image Classification
College of Information Science and Engineering, Northeastern University（东北大学信息科学与工程学院）；Foshan Graduate School, Northeastern University（东北大学佛山研究生院）；Faculty of Robot Science and Engineering, Northeastern University（东北大学机器人科学与工程学院）；State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University（东北大学流程工业综合自动化国家重点实验室）；School of Intelligent Systems Engineering, Sun Yat-Sen University（中山大学智能系统工程学院）；Guangdong Provincial Key Laboratory of Fire Science and Technology（广东省火灾科学与技术重点实验室）
University of Science Technology of China（中国科学技术大学）；University of Maryland College Park（马里兰大学帕克分校）；JD Explore Academy（京东探索研究院）；The University of Sydney（悉尼大学）
Data augmentation is a critical contributing factor to the success of deep learning but heavily relies on prior domain knowledge which is not always available. Recent works on automatic data augmentation learn a policy to form a sequence of augmentation operations, which are still pre-defined and restricted to limited options. In this paper, we show that a prior-free autonomous data augmentation's objective can be derived from a representation learning principle that aims to preserve the minimum sufficient information of the labels. Given an example, the objective aims at creating a distant "h…
Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning
Southeast University（东南大学）；Centre de Recherche en Information Biomédicale Sino-Français (CRIBs)（中法生物医学信息研究中心（CRIBs））；Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing（江苏省医疗信息处理联合国际研究实验室）；School of Instrument Science and Engineering, Southeast University（东南大学仪器科学与工程学院）；Western University（西安大略大学）；Case Western Reserve University（凯斯西储大学）
Uncertainty-aware Cross-training for Semi-supervised Medical Image 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）；Institute of High Performance Computing, A*STAR（A*STAR高性能计算研究所）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；School of Artificial Intelligence and Computer Science, Jiangnan University（江南大学人工智能与计算机科学学院）
FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation
School of Artificial Intelligence and Computer Science, Nantong University, China（人工智能与计算机科学学院，南通大学）；School of Information Science and Technology, Nantong University, China（信息科学与技术学院，南通大学）
ContourDiff: Unpaired Medical Image Translation with Structural Consistency
Department of Electrical and Computer Engineering, Duke University（杜克大学电气与计算机工程系）；Department of Biostatistics & Bioinformatics, Duke University（杜克大学生物统计学与生物信息学系）；Department of Radiology, Duke University（杜克大学放射学系）；Department of Computer Science, Duke University（杜克大学计算机科学系）
Medical Image De-Identification Benchmark Challenge
Frederick National Laboratory for Cancer Research（弗雷德里克国家癌症研究中心）；National Cancer Institute（国家癌症研究所）；University of Arkansas for Medical Sciences（亚利桑那医学科学大学）；PixelMed Publishing（PixelMed出版公司）；Essential Software Inc.（Essential软件公司）；Amazon Web Services (AWS)（亚马逊网络服务（AWS））；University of California（加州大学）；Cedars-Sinai Medical Center（Cedars-Sinai医疗中心）；Stanford University（斯坦福大学）；Biomedical Engineering & Imaging Institute（生物医学工程与成像研究所）；Icahn School of Medicine at Mount Sinai（辛格纳医学中心学院）；University Medical Center Freiburg（弗赖堡大学医学中心）；German Cancer Research Center（德国癌症研究中心）；Heidelberg University Hospital（海德堡大学医院）；GE Healthcare（通用电气医疗）；ShanghaiTech University（上海科技大学）；Vanderbilt University Medical Center（范德比尔大学医学中心）
University of Pittsburgh（匹兹堡大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Pittsburgh Medical School（匹兹堡大学医学院）
We propose a BlackBox Counterfactual Explainer, designed to explain image classification models for medical applications. Classical approaches (e.g., saliency maps) that assess feature importance do not explain "how" imaging features in important anatomical regions are relevant to the classification decision. Our framework explains the decision for a target class by gradually "exaggerating" the semantic effect of the class in a query image. We adopted a Generative Adversarial Network (GAN) to generate a progressive set of perturbations to a query image, such that the classification decision ch…
SRUG: A Fusion-Driven Generator Network for Medical Image Translation
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science（上海工程技术大学电子电气工程学院）；Department of Thoracic Surgery, Zhongshan Hospital of Fudan University（复旦大学中山医院胸外科）；Clinical Research Unit, Institute of Clinical Science, Zhongshan Hospital of Fudan University（复旦大学中山医院临床科研部）；Department of Medical Oncology, Cancer Center, and Fudan Zhangjiang Institute, Zhongshan Hospital of Fudan University（复旦大学中山医院医学肿瘤科、癌症中心及复旦张江研究院）
University of Tokyo（东京大学）；Kyushu University（九州大学）；University of Tokyo Hospital（东京大学附属医院）；Hokkaido University（北海道大学）；Tokushima University（德岛大学）
In recent years, deep-learning-based image processing has emerged as a valuable tool for medical imaging owing to its high performance. However, the quality of deep-learning-based methods heavily relies on the amount of training data; the high cost of acquiring a large dataset is a limitation to their utilization in medical fields. Herein, based on deep learning, we developed a computed tomography (CT) modality conversion method requiring only a few unsupervised images. The proposed method is based on CycleGAN with several extensions tailored for CT images, which aims at preserving the structu…
ConvFormer: Plug-and-Play CNN-Style Transformers for Improving Medical Image Segmentation
Huazhong University of Science and Technology（华中科技大学）；Union Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院附属协和医院）
U-RWKV: Lightweight medical image segmentation with direction-adaptive RWKV
School of Biomedical Engineering, Division of Life Sciences；Medicine, University of Science；Technology of China (USTC), Hefei Anhui, 230026, China Center for Medical Imaging, Robotics, Analytic Computing \& Learning (MIRACLE), Suzhou Institute for Advance Research, USTC Jiangsu Provincial Key Laboratory of Multimodal Digital Twin Technology, Suzhou State Key Laboratory of Precision
