Spatially Dependent U-Nets: Highly Accurate Architectures for Medical Imaging Segmentation
Institute for Machine Learning, ETH Zurich（苏黎世联邦理工学院机器学习研究所）；Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等技术学院）；Champalimaud Foundation（尚帕利莫基金会）
João B. S. Carvalho, João A. Santinha, Đorđe Miladinović, Joachim M. Buhmann
In clinical practice, regions of interest in medical imaging often need to be identified through a process of precise image segmentation. The quality of this image segmentation step critically affects the subsequent clinical assessment of the patient status. To enable high accuracy, automatic image segmentation, we introduce a novel deep neural network architecture that exploits the inherent spatial coherence of anatomical structures and is well equipped to capture long-range spatial dependencies in the segmented pixel/voxel space. In contrast to the state-of-the-art solutions based on convolutional layers, our approach leverages on recently introduced spatial dependency layers that have an unbounded receptive field and explicitly model the inductive bias of spatial coherence. Our method performs favourably to commonly used U-Net and U-Net++ architectures as demonstrated by improved Dice and Jaccardscore in three different medical segmentation tasks: nuclei segmentation in microscopy images, polyp segmentation in colonoscopy videos, and liver segmentation in abdominal CT scans.
MSE-Nets: Multi-annotated Semi-supervised Ensemble Networks for Improving Segmentation of Medical Image with Ambiguous Boundaries
Hangzhou Dianzi University（杭州电子科技大学）；Zhejiang Sci-Tech University（浙江理工大学）；Shandong University（山东大学）；Lishui University（丽水学院）；Center（研究中心）；Communication University of Zhejiang（浙江传媒学院）
IB-U-Nets: Improving medical image segmentation tasks with 3D Inductive Biased kernels
Medical Center University of Freiburg（弗莱堡大学医学中心）；Faculty of Medicine, University of Freiburg（弗莱堡大学医学院）；German Cancer Consortium (DKTK)（德国癌症联盟（DKTK））；German Oncology Center, European University（欧洲大学德国肿瘤学中心）
Robust registration of medical images in the presence of spatially-varying noise
University of California, San Francisco（加州大学旧金山分校）；Weill Institute for Neurosciences（威尔神经科学研究所）；Iran Science and Technology University（伊朗科技大学）；Sharif University of Technology（谢里夫理工大学）
Adapting a Segmentation Foundation Model for Medical Image Classification
Department of Computer Science, University of Texas Rio Grande Valley（德克萨斯理工大学里奥格兰德分校计算机科学系）；Department of Computer Science, Texas Woman’s University（德克萨斯女性大学计算机科学系）；Sewickley Academy（塞维克利学院）；Department of Mathematical Sciences, The University of Texas at Dallas（德克萨斯大学达拉斯分校数学科学系）；Department of Computer Science and Engineering, University of Notre Dame（诺特丹大学计算机科学与工程系）
本文提出将 SAM 图像编码器作为冻结特征提取器，并设计空间局部化通道注意力机制 SLCA，将其集成到深度学习分类模型中，以增强对关键区域的关注，在三个公开医学图像分类数据集上验证了方法的有效性和数据高效性。
SDCNet: Smoothed Dense-Convolution Network for Restoring Low-Dose Cerebral CT Perfusion
University of Florida（佛罗里达大学）
随着公众对医学成像中累积的辐射暴露引起的潜在癌症风险和健康危害的大量关注，减少基于X射线的医学成像（诸如计算机断层扫描灌注（CTP））中的辐射剂量已经引起了重大的研究兴趣。在本文中，我们采用基于深度卷积神经网络（CNN）的方法，并引入平滑密集卷积神经网络（SDCNet）来从低剂量图像恢复高剂量质量CTP图像。SDCNet由通过跳跃连接级联的子网络块组成，以从成对的低/高剂量CT扫描中推断噪声（差异）。SDCNet可以有效地去除实际低剂量CT扫描中的噪声，提高医学图像的质量。我们评估了数千个CT灌注帧的重建图像去噪和灌注图量化，包括脑血流量（CBF）和脑血容量（CBV）的建议架构。SDCNet在视觉和定量结果方面都实现了高性能，具有良好的计算效率，与最先进的方法相比毫不逊色。\textit{The code is available at \url{https://github.com/cswin/RC-Nets}}。
Unsupervised Multi-Modality Registration Network based on Spatially Encoded Gradient Information
College of Physics and Information Engineering, Fuzhou University（福州大学物理与信息工程学院）；School of Data Science, Fudan University（复旦大学大数据学院）；School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）
PRS-Med: Position Reasoning Segmentation in Medical Imaging
Aalto University（阿尔托大学）；Northwestern University（西北大学）；Technical University of Denmark（丹麦技术大学）；Chongqing University of Posts and Telecommunications（重庆邮电大学）；University of South Dakota（南达科他大学）
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）
Spinal cord gray matter segmentation using deep dilated convolutions
Polytechnique Montreal（蒙特利尔高等理工学院）；Duke University Medical Center（杜克大学医学中心）；University of California San Francisco（加州大学旧金山分校）；Université de Montréal（蒙特利尔大学）
Bridging Vision Foundation Model Priors with CLIP for Spatial-aware Few-shot Anomaly Detection in Medical Images
The Chinese University of Hong Kong（香港中文大学）；The University of Hong Kong（香港大学）；Institute of Medical Intelligence and XR, The Chinese University of Hong Kong（香港中文大学医学智能与扩展现实研究所）
3D Transport-based Morphometry (3D-TBM) for medical image analysis
Department of Computer Engineering University of Virginia（计算机工程系，弗吉尼亚大学）；Department of Biomedical Engineering University of Virginia（生物医学工程系，弗吉尼亚大学）；Department of Electrical and Computer Engineering University of Virginia（电气与计算机工程系，弗吉尼亚大学）；Department of Neurological Surgery University of Virginia（神经外科系，弗吉尼亚大学）；Department of Radiology Washington University in St. Louis（放射科，华盛顿大学圣路易斯分校）；Department of Electrical and Systems Engineering Washington University in St. Louis（电气与系统工程系，华盛顿大学圣路易斯分校）
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）
MVC-Net: A Convolutional Neural Network Architecture for Manifold-Valued Images With Applications
University of Florida（佛罗里达大学）；Department of CISE, University of Florida（佛罗里达大学CISE系）；Department of Statistics, University of Florida（佛罗里达大学统计系）；Department of Applied Physiology and Kinesiology, University of Florida（佛罗里达大学应用生理学与运动学系）；CISE, University of Florida（佛罗里达大学CISE）
