Multi-level Context Gating of Embedded Collective Knowledge for Medical Image Segmentation
Institute for Research in Fundamental Sciences (IPM)（基础科学研究所（IPM））；Sharif University of Technology（谢里夫理工大学）；Universitat de Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）
Medical image segmentation has been very challenging due to the large variation of anatomy across different cases. Recent advances in deep learning frameworks have exhibited faster and more accurate performance in image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net for medical image segmentation, in which we take full advantages of U-Net, Squeeze and Excitation (SE) block, bi-directional ConvLSTM (BConvLSTM), and the mechanism of dense convolutions. (I) We improve the segmentation performance by utilizing SE modules within the U-Net, with a minor effect on model complexity. These blocks adaptively recalibrate the channel-wise feature responses by utilizing a self-gating mechanism of the global information embedding of the feature maps. (II) To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path. (III) Instead of a simple concatenation in the skip connection of U-Net, we employ BConvLSTM in all levels of the network to combine the feature maps extracted from the corresponding encoding path and the previous decoding up-convolutional layer in a non-linear way. The proposed model is evaluated on six datasets DRIVE, ISIC 2017 and 2018, lung segmentation, $PH^2$, and cell nuclei segmentation, achieving state-of-the-art performance.
EEMS: Edge-Prompt Enhanced Medical Image Segmentation Based on Learnable Gating Mechanism
School of Advanced Manufacturing, Guangdong University of Technology（广东技术大学先进制造学院）；Department of Cardiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China（广东省人民医院心内科（广东省医学科学院））；College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China（南京邮电大学自动化学院）
Towards Collective Intelligence: Uncertainty-aware SAM Adaptation for Ambiguous Medical Image Segmentation
Department of Computer Science, The University of Alabama at Birmingham（阿拉巴马大学伯明翰分校计算机科学系）；Doctoral Training Centre, University of Oxford（牛津大学博士培训中心）；Department of Biomedical Engineering and Department of Electrical and Computer Engineering, National University of Singapore（新加坡国立大学生物医学工程系和电气与计算机工程系）；Computational Biology Department, Carnegie Mellon University（卡内基梅隆大学计算生物学系）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Sichuan Normal University（四川师范大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Transformers have achieved remarkable success in medical image analysis owing to their powerful capability to use flexible self-attention mechanism. However, due to lacking intrinsic inductive bias in modeling visual structural information, they generally require a large-scale pre-training schedule, limiting the clinical applications over expensive small-scale medical data. To this end, we propose a parameter-efficient transformer to explore intrinsic inductive bias via position information for medical image segmentation. Specifically, we empirically investigate how different position encoding…
SAM-Med3D-MoE: Towards a Non-Forgetting Segment Anything Model via Mixture of Experts for 3D Medical Image Segmentation
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；College of Computer Science, East China Normal University（华东师范大学计算机科学学院）；Department of Data Science and AI, Faculty of IT, Monash University（莫纳什大学信息技术学院数据科学与人工智能系）；College of Computer Science, Sichuan University（四川大学计算机学院）
Difference-Driven Gating: Adaptive Feature Fusion for U-Net Decoder
Department of Computer Science and Technology, Institute for Artificial Intelligence, BNRist, IDG/McGovern Institute for Brain Research, Tsinghua University（清华大学计算机科学与技术系、人工智能研究院、北京信息科学与技术国家研究中心、IDG/麦戈文脑科学研究院）；Chinese Institute for Brain Research (CIBR)（中国脑科学研究院）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机与信息技术学院）
TM-UNet: Token-Memory Enhanced Sequential Modeling for Efficient Medical Image Segmentation
Dalian University of Technology（大连理工大学）；University of Lincoln（林肯大学）；University of Nottingham（诺丁汉大学）；Waseda University（早稻田大学）；University of Nottingham Ningbo China（宁波诺丁汉大学）；Yale University（耶鲁大学）
CoMLP: Cooperatively-Gated MLPs for Fine-Grained Cross-Modal Information Fusion in Medical Image Segmentation
Institute of Translational Medicine, Shanghai Jiao Tong University（上海交通大学转化医学研究院）；Zhongguancun Academy（中关村学院）；Zhongguancun Institute of Artificial Intelligence（中关村人工智能研究院）；School of Computer Science, the University of Sydney（悉尼大学计算机学院）；China United Network Communications Group Co., Ltd.（中国联合网络通信集团有限公司）
DTEA: Dynamic Topology Weaving and Instability-Driven Entropic Attenuation for Medical Image Segmentation
School of Advanced Manufacturing, Guangdong University of Technology（广东技术大学先进制造学院）；School of Electronic and Communication Engineering, Shenzhen Polytechnic University（深圳职业技术大学电子与通信工程学院）；Guangzhou City University of Technology（广州市城市技术大学）；Faculty of Science and Technology, University of Macau（澳门大学科学与技术学院）；Department of Cardiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University（广东省人民医院心内科（广东省医学科学院））；Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences（广东省心血管病研究所，广东省人民医院，广东省医学科学院）
TopoMamba: Topology-Aware Scanning and Fusion for Segmenting Heterogeneous Medical Visual Media
Dalian University of Technology（大连理工大学）；Guangdong University of Technology（广东工业大学）；Huizhou University（惠州市学院）；University of Macau（澳门大学）；Nanjing University of Posts and Telecommunications（南京邮电大学）；Hong Kong Polytechnic University（香港理工大学）
HyM-UNet: Synergizing Local Texture and Global Context via Hybrid CNN-Mamba Architecture for Medical Image Segmentation
School of Information and Communication Engineering, Beijing University of Posts and Communications (BUPT)（信息与通信工程学院，北京邮电大学）；Alibaba Cloud AI Labs（阿里云人工智能实验室）
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）
IMT School for Advanced Studies Lucca（IMT卢卡高等研究院）；University of Edinburgh（爱丁堡大学）
Large, fine-grained image segmentation datasets, annotated at pixel-level, are difficult to obtain, particularly in medical imaging, where annotations also require expert knowledge. Weakly-supervised learning can train models by relying on weaker forms of annotation, such as scribbles. Here, we learn to segment using scribble annotations in an adversarial game. With unpaired segmentation masks, we train a multi-scale GAN to generate realistic segmentation masks at multiple resolutions, while we use scribbles to learn their correct position in the image. Central to the model's success is a nove…
ProSMA-UNet: Decoder Conditioning for Proximal-Sparse Skip Feature Selection
Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK（应用数学与理论物理系，剑桥大学）；School of Engineering, Zurich University of Applied Sciences, CH（工程学院，应用科学大学 Zurich）；Bioengineering Department and ImperialX, Imperial College London, UK（生物工程系和ImperialX，伦敦帝国学院）；Lucerne University Teaching and Research Hospital, CH（卢塞恩大学教学与研究医院）；Yau Mathematical Sciences Center, Tsinghua University, China（尤金数学科学中心，清华大学）
Computer-assisted Applications in Medicine, ETH Zurich（苏黎世联邦理工学院计算机辅助医学应用）；CARD Group, University Hospital Balgrist, University of Zurich（苏黎世大学巴格里斯特大学医院CARD小组）
Deep learning has been widely accepted as a promising solution for medical image segmentation, given a sufficiently large representative dataset of images with corresponding annotations. With ever increasing amounts of annotated medical datasets, it is infeasible to train a learning method always with all data from scratch. This is also doomed to hit computational limits, e.g., memory or runtime feasible for training. Incremental learning can be a potential solution, where new information (images or anatomy) is introduced iteratively. Nevertheless, for the preservation of the collective inform…
IP-SAM: Rethinking Prompt-Conditioned Segmentation for Prompt-Absent Deployment
University of Chinese Academy of Sciences（中国科学院大学）；Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences（中国科学院空间应用工程与技术中心）
The devastation caused by the coronavirus pandemic makes it imperative to design automated techniques for a fast and accurate detection. We propose a novel non-invasive tool, using deep learning and imaging, for delineating COVID-19 infection in lungs. The Ensembling Attention-based Multi-scaled Convolution network (EAMC), employing Leave-One-Patient-Out (LOPO) training, exhibits high sensitivity and precision in outlining infected regions along with assessment of severity. The Attention module combines contextual with local information, at multiple scales, for accurate segmentation. Ensemble …
MedImageInsight: An Open-Source Embedding Model for General Domain Medical Imaging
Microsoft Health and Life Sciences（微软健康与生命科学部门）；Microsoft Research（微软研究院）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Microsoft Azure AI（微软Azure人工智能部门）
Umeå University（于默奥大学）；Eindhoven University of Technology（埃因霍温理工大学）
Kidney tumor segmentation emerges as a new frontier of computer vision in medical imaging. This is partly due to its challenging manual annotation and great medical impact. Within the scope of the Kidney Tumor Segmentation Challenge 2019, that is aiming at combined kidney and tumor segmentation, this work proposes a novel combination of 3D U-Nets---collectively denoted TuNet---utilizing the resulting kidney masks for the consecutive tumor segmentation. The proposed method achieves a Sørensen-Dice coefficient score of 0.902 for the kidney, and 0.408 for the tumor segmentation, computed from a f…
Deep Learning-Based Pneumonia Detection from Chest X-ray Images: A CNN Approach with Performance Analysis and Clinical Implications
Amity Institute of Bio Technology, Amity University Kolkata（阿米蒂大学加尔各答分校阿米蒂生物技术学院）；Dept of Computer Science, Heritage Institute of Technology, Kolkata（加尔各答遗产技术学院计算机科学系）；School of Engineering and Technology, Amity University Kolkata（阿米蒂大学加尔各答分校工程与技术学院）
