IEEE Transactions on Medical Imaging最新文献_第5页

UTSRMorph: A Unified Transformer and Superresolution Network for Unsupervised Medical Image Registration UTSRMorph：用于无监督医学图像配准的统一变换器和超分辨率网络

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2024-09-25 DOI: 10.1109/tmi.2024.3467919

Runshi Zhang, Hao Mo, Junchen Wang, Bimeng Jie, Yang He, Nenghao Jin, Liang Zhu

引用次数: 0

Enhanced Multimodal Low-rank Embedding based Feature Selection Model for Multimodal Alzheimer's Disease Diagnosis. 基于低等级嵌入的增强型多模态特征选择模型用于多模态阿尔茨海默病诊断

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2024-09-20 DOI: 10.1109/tmi.2024.3464861

Zhi Chen,Yongguo Liu,Yun Zhang,Jiajing Zhu,Qiaoqin Li,Xindong Wu

{"title":"Enhanced Multimodal Low-rank Embedding based Feature Selection Model for Multimodal Alzheimer's Disease Diagnosis.","authors":"Zhi Chen,Yongguo Liu,Yun Zhang,Jiajing Zhu,Qiaoqin Li,Xindong Wu","doi":"10.1109/tmi.2024.3464861","DOIUrl":"https://doi.org/10.1109/tmi.2024.3464861","url":null,"abstract":"Identification of Alzheimer's disease (AD) with multimodal neuroimaging data has been receiving increasing attention. However, the presence of numerous redundant features and corrupted neuroimages within multimodal datasets poses significant challenges for existing methods. In this paper, we propose a feature selection method named Enhanced Multimodal Low-rank Embedding (EMLE) for multimodal AD diagnosis. Unlike previous methods utilizing convex relaxations of the ℓ2,0-norm, EMLE exploits an ℓ2,γ-norm regularized projection matrix to obtain an embedding representation and select informative features jointly for each modality. The ℓ2,γ-norm, employing an upper-bounded nonconvex Minimax Concave Penalty (MCP) function to characterize sparsity, offers a superior approximation for the ℓ2,0-norm compared to other convex relaxations. Next, a similarity graph is learned based on the self-expressiveness property to increase the robustness to corrupted data. As the approximation coefficient vectors of samples from the same class should be highly correlated, an MCP function introduced norm, i.e., matrix γ-norm, is applied to constrain the rank of the graph. Furthermore, recognizing that diverse modalities should share an underlying structure related to AD, we establish a consensus graph for all modalities to unveil intrinsic structures across multiple modalities. Finally, we fuse the embedding representations of all modalities into the label space to incorporate supervisory information. The results of extensive experiments on the Alzheimer's Disease Neuroimaging Initiative datasets verify the discriminability of the features selected by EMLE.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"13 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142275168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IPNet: An Interpretable Network with Progressive Loss for Whole-stage Colorectal Disease Diagnosis. IPNet：用于全阶段结直肠疾病诊断的渐进损失可解释网络

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2024-09-19 DOI: 10.1109/tmi.2024.3459910

Junhu Fu,Ke Chen,Qi Dou,Yun Gao,Yiping He,Pinghong Zhou,Shengli Lin,Yuanyuan Wang,Yi Guo

{"title":"IPNet: An Interpretable Network with Progressive Loss for Whole-stage Colorectal Disease Diagnosis.","authors":"Junhu Fu,Ke Chen,Qi Dou,Yun Gao,Yiping He,Pinghong Zhou,Shengli Lin,Yuanyuan Wang,Yi Guo","doi":"10.1109/tmi.2024.3459910","DOIUrl":"https://doi.org/10.1109/tmi.2024.3459910","url":null,"abstract":"Colorectal cancer plays a dominant role in cancer-related deaths, primarily due to the absence of obvious early-stage symptoms. Whole-stage colorectal disease diagnosis is crucial for assessing lesion evolution and determining treatment plans. However, locality difference and disease progression lead to intra-class disparities and inter-class similarities for colorectal lesion representation. In addition, interpretable algorithms explaining the lesion progression are still lacking, making the prediction process a \"black box\". In this paper, we propose IPNet, a dual-branch interpretable network with progressive loss for whole-stage colorectal disease diagnosis. The dual-branch architecture captures unbiased features representing diverse localities to suppress intra-class variation. The progressive loss function considers inter-class relationship, using prior knowledge of disease evolution to guide classification. Furthermore, a novel Grain-CAM is designed to interpret IPNet by visualizing pixel-wise attention maps from shallow to deep layers, providing regions semantically related to IPNet's progressive classification. We conducted whole-stage diagnosis on two image modalities, i.e., colorectal lesion classification on 129,893 endoscopic optical images and rectal tumor T-staging on 11,072 endoscopic ultrasound images. IPNet is shown to surpass other state-of-the-art algorithms, accordingly achieving an accuracy of 93.15% and 89.62%. Especially, it establishes effective decision boundaries for challenges like polyp vs. adenoma and T2 vs. T3. The results demonstrate an explainable attempt for colorectal lesion classification at a whole-stage level, and rectal tumor T-staging by endoscopic ultrasound is also unprecedentedly explored. IPNet is expected to be further applied, assisting physicians in whole-stage disease diagnosis and enhancing diagnostic interpretability.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"21 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142273441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Source Extent Estimation in OPM-MEG: A Two-Stage Champagne Approach OPM-MEG 中的源范围估算：两阶段香槟法

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2024-09-17 DOI: 10.1109/tmi.2024.3462415

Wen Li, Fuzhi Cao, Nan An, Wenli Wang, Chunhui Wang, Weinan Xu, Yang Gao, Xiaolin Ning

引用次数: 0

Subjective and Objective Quality Assessment of Colonoscopy Videos 结肠镜检查视频的主观和客观质量评估

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2024-09-16 DOI: 10.1109/tmi.2024.3461737

Guanghui Yue, Lixin Zhang, Jingfeng Du, Tianwei Zhou, Wei Zhou, Weisi Lin

引用次数: 0

M2DC: A Meta-Learning Framework for Generalizable Diagnostic Classification of Major Depressive Disorder M2DC：重度抑郁障碍通用诊断分类的元学习框架

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2024-09-16 DOI: 10.1109/tmi.2024.3461312

Jianpo Su, Bo Wang, Zhipeng Fan, Yifan Zhang, Ling-Li Zeng, Hui Shen, Dewen Hu

引用次数: 0

A Multi-Perspective Self-Supervised Generative Adversarial Network for FS to FFPE Stain Transfer 用于 FS 到 FFPE 染色转移的多视角自监督生成对抗网络

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2024-09-16 DOI: 10.1109/tmi.2024.3460795

Yiyang Lin, Yifeng Wang, Zijie Fang, Zexin Li, Xianchao Guan, Danling Jiang, Yongbing Zhang

引用次数: 0

Prototype-Guided Graph Reasoning Network for Few-Shot Medical Image Segmentation 原型引导的图推理网络用于少量医疗图像分割

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2024-09-13 DOI: 10.1109/tmi.2024.3459943

Wendong Huang, Jinwu Hu, Junhao Xiao, Yang Wei, Xiuli Bi, Bin Xiao

引用次数: 0

Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images 用于脑图像反事实生成和异常检测的扩散模型

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2024-09-13 DOI: 10.1109/tmi.2024.3460391

Alessandro Fontanella, Grant Mair, Joanna Wardlaw, Emanuele Trucco, Amos Storkey

引用次数: 0

Prior-knowledge Embedded U-Net based Fully Automatic Vessel Wall Volume Measurement of the Carotid Artery in 3D Ultrasound Image 基于先验知识的嵌入式 U-Net 全自动测量三维超声图像中的颈动脉血管壁容积

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2024-09-10 DOI: 10.1109/tmi.2024.3457245

Zheng Yue, Jiayao Jiang, Wenguang Hou, Quan Zhou, J. David Spence, Aaron Fenster, Wu Qiu, Mingyue Ding

引用次数: 0