Global and Local Mamba Network for Multi-Modality Medical Image Super-Resolution
School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；Hunan Engineering Research Center of Machine Vision and Intelligent Medicine, Central South University（中南大学机器视觉与智能医学湖南省工程研究中心）；University of Rouen-Normandy（鲁昂-诺曼底大学）；Henri Becquerel Cancer Center（亨利·贝克勒尔癌症中心）
Zexin Ji, Beiji Zou, Xiaoyan Kui, Sebastien Thureau, Su Ruan
Convolutional neural networks and Transformer have made significant progresses in multi-modality medical image super-resolution. However, these methods either have a fixed receptive field for local learning or significant computational burdens for global learning, limiting the super-resolution performance. To solve this problem, State Space Models, notably Mamba, is introduced to efficiently model long-range dependencies in images with linear computational complexity. Relying on the Mamba and the fact that low-resolution images rely on global information to compensate for missing details, while high-resolution reference images need to provide more local details for accurate super-resolution, we propose a global and local Mamba network (GLMamba) for multi-modality medical image super-resolution. To be specific, our GLMamba is a two-branch network equipped with a global Mamba branch and a local Mamba branch. The global Mamba branch captures long-range relationships in low-resolution inputs, and the local Mamba branch focuses more on short-range details in high-resolution reference images. We also use the deform block to adaptively extract features of both branches to enhance the representation ability. A modulator is designed to further enhance deformable features in both global and local Mamba blocks. To fully integrate the reference image for low-resolution image super-resolution, we further develop a multi-modality feature fusion block to adaptively fuse features by considering similarities, differences, and complementary aspects between modalities. In addition, a contrastive edge loss (CELoss) is developed for sufficient enhancement of edge textures and contrast in medical images.
Mamba-Based Modality Disentanglement Network for Multi-Contrast MRI Reconstruction
School of Communication and Information Engineering, Shanghai University（上海大学通信与信息工程学院）；School of Computer Science and Technology, East China Normal University（东华大学计算机科学与技术学院）
MambaMorph: a Mamba-based Framework for Medical MR-CT Deformable Registration
Image Processing Center, Beihang University, Beijing, China（北京航空航天大学图像处理中心）；Robotics Institute, Beihang University, Beijing, China（北京航空航天大学机器人研究所）；Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China（华中科技大学同济医院）
MambaMIM: Pre-training Mamba with State Space Token Interpolation and its Application to Medical Image Segmentation
School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China（科学技术大学生物医学工程学院）；Suzhou Institute for Advanced Research, University of Science and Technology of China（科学技术大学苏州市先进研究所）；Center for Medical Imaging, Robotics, and Analytic Computing & LEarning (MIRACLE), Suzhou Institute for Advanced Research, USTC（医学影像、机器人与分析计算与学习中心）；Department of Automation, Shanghai Jiao Tong University（上海交通大学自动化系）；Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University（上海交通大学图像处理与模式识别研究所）；Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医学机器人研究所）；State Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China（科学技术大学精密与智能化学国家重点实验室）
MambaMIR: An Arbitrary-Masked Mamba for Joint Medical Image Reconstruction and Uncertainty Estimation
Imperial College London（伦敦帝国学院）；Royal Brompton Hospital（皇家布朗普顿医院）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；University of Cambridge（剑桥大学）
Mamba Based Feature Extraction And Adaptive Multilevel Feature Fusion For 3D Tumor Segmentation From Multi-modal Medical Image
School of Computer Science and Engineering, Central South University, Changsha, 410083, China（计算机科学与工程学院，中南大学，长沙，410083，中国）；Hunan Engineering Research Center of Machine Vision and Intelligent Medicine, Central South University, Changsha, 410083, China（机器视觉与智能医学工程研究中心，中南大学，长沙，410083，中国）；Department of Radiation Oncology, Washington University in St. Louis, USA（放射肿瘤科，华盛顿大学圣路易斯分校，美国）；Department of Nuclear Medicine, Henri Becquerel Cancer Center, Rouen, France（核医学科，亨利·贝克勒尔癌症中心，鲁昂，法国）；University of Rouen-Normandy, AMIS - QuantIF UR 4108, F-76000, Rouen, France（鲁昂-诺曼底大学，AMIS - QuantIF UR 4108，法国，F-76000，鲁昂，法国）
Self-Prior Guided Mamba-UNet Networks for Medical Image Super-Resolution
School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；Hunan Engineering Research Center of Machine Vision and Intelligent Medicine, Central South University（中南大学机器视觉与智能医学湖南省工程研究中心）；Department of Nuclear Medicine, Henri Becquerel Cancer Center（亨利·贝克勒尔癌症中心核医学科）；University of Rouen-Normandy（鲁昂-诺曼底大学）
A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyond
Indian Institute of Technology Indore, India；R.M.D. Engineering College, Kavaraipettai, India；Jio Platforms Limited, India；Birla Institute of Technology \& Science Pilani, India
HybridMamba: A Dual-domain Mamba for 3D Medical Image Segmentation
The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；Department of Radiology, Fudan University Shanghai Cancer Center（复旦大学上海肿瘤中心放射科）；Department of Oncology, Shanghai Medical College, Fudan University（复旦大学上海医学院肿瘤科）；The Hong Kong University of Science and Technology（香港科学与技术大学）
XFMamba: Cross-Fusion Mamba for Multi-View Medical Image Classification
Digital Environment Research Institute (DERI), Queen Mary University of London（伦敦大学玛丽女王学院数字环境研究所（DERI））；School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦大学玛丽女王学院电子工程与计算机科学学院）；Blizard Institute - Faculty of Medicine and Dentistry, Queen Mary University of London（伦敦大学玛丽女王学院医学与牙科学院布利扎德研究所）；University of Cambridge（剑桥大学）
MMR-Mamba: Multi-Modal MRI Reconstruction with Mamba and Spatial-Frequency Information Fusion
The Hong Kong Polytechnic University（香港理工大学）；University of Science and Technology of China（中国科学技术大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Stanford University（斯坦福大学）
Backbone-Conditional Behavior of Modality Gating in Multi-Modal Prostate MRI Segmentation: A 5-Fold Cross-Validation and Gate Mechanism Analysis
The Second Xiangya Hospital of Central South University（中南大学湘雅医学院第二医院）；The Third Xiangya Hospital of Central South University（中南大学湘雅医学院第三医院）；School of Life Sciences, Central South University（中南大学生命科学学院）；Hunan Provincial Key Laboratory of Medical Information Research (Central South University)（湖南省医学信息研究重点实验室（中南大学））；Hunan Provincial Clinical Medical Research Center for Cardiovascular Intelligent Medicine（湖南省心血管智能医学临床医学研究中心）
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）
Mamba-driven MRI-to-CT Synthesis for MRI-only Radiotherapy Planning
School of Electrical；Computer Engineering, National Technical University of Athens Archimedes, Athena Research Center CentraleSup\'elec, University of Paris-Saclay
This paper presents a new approach of transfer learning-based medical image classification to mitigate insufficient labeled data problem in medical domain. Instead of direct transfer learning from source to small number of labeled target data, we propose a modality-bridge transfer learning which employs the bridge database in the same medical imaging acquisition modality as target database. By learning the projection function from source to bridge and from bridge to target, the domain difference between source (e.g., natural images) and target (e.g., X-ray images) can be mitigated. Experimenta…
MedMamba: Vision Mamba for Medical Image Classification
School of Biomedical Engineering（生物医学工程学院）；Guangzhou Medical University（广州医科大学）；School of Mathematics and Systems Science（数学与系统科学学院）；Guangdong Polytechnic Normal University（广东技术师范大学）
I2I-Mamba: Multi-modal medical image synthesis via selective state space modeling
Department of Electrical and Electronics Engineering, and National Magnetic Resonance Research Center, Bilkent University（电子工程系和国家磁共振研究中心，比尔肯特大学）；Department of Electrical Engineering, Stanford University（电气工程系，斯坦福大学）
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）
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）
Attention-Mamba: A Mamba-Enhanced Multi-Scale Parallel Inference Network for Medical Image Segmentation
School of Astronautics, Northwestern Polytechnical University（航天学院，西北工业大学）；Department of Electronics and Telecommunications, Politecnico di Torino（电信系，托斯卡纳理工学院）；Beijing Aerospace Automatic Control Research Institute（北京航天自动控制研究所）；Xi'an University of Posts and Telecommunications（西安邮电大学）；Department of Oncology, University of Turin（肿瘤科，都灵大学）；Candiolo Cancer Institute, FPO-IRCCS（坎迪奥利癌症研究所，FPO-IRCCS）
MambaControl: Anatomy Graph-Enhanced Mamba ControlNet with Fourier Refinement for Diffusion-Based Disease Trajectory Prediction
Faculty of Applied Sciences, Macao Polytechnic University（应用科学学院，澳门理工学院）；Department of Electrical Engineering, Zhejiang University（电气工程学院，浙江大学）；Department of Computer Science, The University of Adelaide（计算机科学学院，阿德莱德大学）；Department of Computer Science, University of Birmingham（计算机科学学院，伯明翰大学）
HGP-Mamba: Integrating Histology and Generated Protein Features for Mamba-based Multimodal Survival Risk Prediction
Cancer Hospital of Dalian University of Technology, Shenyang, China（大连理工大学沈阳医院）；School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, China（大连理工大学生物医学工程学院）；Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian University of Technology, Dalian, China（大连理工大学集成电路与生物医学电子系统重点实验室）
Medical imaging is a cornerstone of therapy and diagnosis in modern medicine. However, the choice of imaging modality for a particular theranostic task typically involves trade-offs between the feasibility of using a particular modality (e.g., short wait times, low cost, fast acquisition, reduced radiation/invasiveness) and the expected performance on a clinical task (e.g., diagnostic accuracy, efficacy of treatment planning and guidance). In this work, we aim to apply the knowledge learned from the less feasible but better-performing (superior) modality to guide the utilization of the more-fe…
Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration
DAMO Academy, Alibaba Group（阿里巴巴达摩院）；Hupan Lab（湖畔实验室）；College of Computer Science and Technology, Zhejiang University, China（浙江大学计算机科学与技术学院）；Shengjing Hospital of China Medical University, China（中国医科大学附属盛京医院）
Cross-modality image estimation involves the generation of images of one medical imaging modality from that of another modality. Convolutional neural networks (CNNs) have been shown to be useful in identifying, characterising and extracting image patterns. Generative adversarial networks (GANs) use CNNs as generators and estimated images are discriminated as true or false based on an additional network. CNNs and GANs within the image estimation framework may be considered more generally as deep learning approaches, since imaging data tends to be large, leading to a larger number of network wei…
School of Computer Science and Technology, Anhui University（计算机科学与技术学院，安徽大学）；School of Electronic and Information Engineering, Anhui University（电子与信息工程学院，安徽大学）
Computation-Efficient Era: A Comprehensive Survey of State Space Models in Medical Image Analysis
School of Biomedical Engineering（生物医学工程学院）；University of British Columbia（不列颠哥伦比亚大学）；Tarbiat Modares University（塔比亚特·莫达雷斯大学）；University of Regensburg（雷根斯堡大学）；South Dakota State University（南达科他州立大学）；RWTH Aachen University（亚琛工业大学）；Lunit Inc.（Lunit公司）；Northwestern University（西北大学）；Fraunhofer Institute for Digital Medicine（弗劳恩霍夫数字医学研究院）
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）
FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images
College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, Shanxi, 030024, China（计算机科学与技术学院（数据科学学院），太原科技大学，太原，山西，030024，中国）；College of Artificial Intelligence, Taiyuan University of Technology, Taiyuan, Shanxi, 030024, China（人工智能学院，太原科技大学，太原，山西，030024，中国）；School of mathematics, Taiyuan University of Technology, Taiyuan, 030024, China（数学学院，太原科技大学，太原，030024，中国）；School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China（人工智能学院，北京师范大学，北京，100875，中国）；National Institute for Data Science in Health（健康数据科学国家研究院）；the Department of Computer Science, School of Informatics, Xiammen, 361005, China（信息学院计算机科学系，厦门，361005，中国）；School of Software, Dalian University of Technology, Dalian,116621, China（软件学院，大连理工大学，大连，116621，中国）
Rutgers University（罗格斯大学）；SenseBrain Research（SenseBrain研究院）；National Heart Centre Singapore（新加坡国家心脏中心）；Duke-National University Of Singapore（杜克-新加坡国立大学）
Multi-modality images have been widely used and provide comprehensive information for medical image analysis. However, acquiring all modalities among all institutes is costly and often impossible in clinical settings. To leverage more comprehensive multi-modality information, we propose a privacy secured decentralized multi-modality adaptive learning architecture named ModalityBank. Our method could learn a set of effective domain-specific modulation parameters plugged into a common domain-agnostic network. We demonstrate by switching different sets of configurations, the generator could outpu…
Simon Fraser University（西蒙弗雷泽大学）；The University of British Columbia（不列颠哥伦比亚大学）
Being able to explain the prediction to clinical end-users is a necessity to leverage the power of AI models for clinical decision support. For medical images, saliency maps are the most common form of explanation. The maps highlight important features for AI model's prediction. Although many saliency map methods have been proposed, it is unknown how well they perform on explaining decisions on multi-modal medical images, where each modality/channel carries distinct clinical meanings of the same underlying biomedical phenomenon. Understanding such modality-dependent features is essential for c…
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）
The Chinese University of Hong Kong（香港中文大学）；Department of Computer Science and Engineering, The Chinese University of Hong Kong（香港中文大学计算机科学与工程学系）；Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology（广东省计算机视觉与虚拟现实技术重点实验室）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
The success of deep convolutional neural networks is partially attributed to the massive amount of annotated training data. However, in practice, medical data annotations are usually expensive and time-consuming to be obtained. Considering multi-modality data with the same anatomic structures are widely available in clinic routine, in this paper, we aim to exploit the prior knowledge (e.g., shape priors) learned from one modality (aka., assistant modality) to improve the segmentation performance on another modality (aka., target modality) to make up annotation scarcity. To alleviate the learni…
Universidad San Francisco de Quito（旧金山基多大学）；University of South Florida（南佛罗里达大学）
Deep learning models have obtained state-of-the-art results for medical image analysis. However, when these models are tested on an unseen domain there is a significant performance degradation. In this work, we present an unsupervised Cross-Modality Adversarial Domain Adaptation (C-MADA) framework for medical image segmentation. C-MADA implements an image- and feature-level adaptation method in a sequential manner. First, images from the source domain are translated to the target domain through an un-paired image-to-image adversarial translation with cycle-consistency loss. Then, a U-Net netwo…
Towards a Universal 3D Medical Multi-modality Generalization via Learning Personalized Invariant Representation
Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）；Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学）；University of Liverpool（利物浦大学）；AI 3 Fudan University（复旦大学）；Duke Kunshan University（杜克-昆山大学）；Zhongshan Hospital（中山医院）
While deep learning methods have shown great success in medical image analysis, they require a number of medical images to train. Due to data privacy concerns and unavailability of medical annotators, it is oftentimes very difficult to obtain a lot of labeled medical images for model training. In this paper, we study cross-modality data augmentation to mitigate the data deficiency issue in the medical imaging domain. We propose a discriminative unpaired image-to-image translation model which translates images in source modality into images in target modality where the translation task is condu…
