Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction
专题命中 医学影像 :MRI(title,abstract);分类 cs.CV、cs.LG、eess.IV;medical image(comments)
Comments 14 pages, 10 figures, published at Medical Image Analysis (MedIA)
科学与医疗
医学智能、临床 AI、医学影像、病理、诊断和医疗健康大模型。
专题命中 医学影像 :MRI(title,abstract);分类 cs.CV、cs.LG、eess.IV;medical image(comments)
Comments 14 pages, 10 figures, published at Medical Image Analysis (MedIA)
专题命中 医学影像 :medical image(title,abstract);分类 cs.CV、cs.LG、eess.IV
Comments 31 pages, 8 figures, 4 tables
Journal ref Medical Image Analysis 84 (2023) 102706
专题命中 医学影像 :medical image(title,abstract);分类 cs.CV、cs.LG、eess.IV
Comments Code: http://github.com/weinajin/multimodal_explanation, Supplementary Material S1 and S2: https://github.com/weinajin/multimodal_explanation/tree/main/paper
Journal ref Medical Image Analysis, 2022
专题命中 医学影像 :MRI(title,abstract);分类 cs.LG、eess.IV、eess.SP;medical image(journal_ref)
Journal ref Medical Image Analysis, Elsevier, 2022, 77, pp.102347
专题命中 医学影像 :CT(title,abstract);分类 cs.CV、cs.LG、eess.IV
Comments There is no correlation between gated and non-gated CT scans causing the points used in the training and results to be flawed. It was inaccurately assumed that there was a correlation between the scans
专题命中 医学影像 :medical image(title,comments);CT(abstract);分类 cs.CV、cs.LG、eess.IV
Comments Published in Medical Image Analysis (extension of MICCAI paper)
专题命中 医学影像 :medical image(title,abstract);分类 cs.CV、cs.LG、eess.IV
Comments Accepted to be presented as an Oral Presentation at 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022. 13 pages. 5 figures
专题命中 医学影像 :medical image(title,abstract);分类 cs.CV、cs.LG、eess.IV
Comments 10 pages, 6 figures, 3 tables. Accepted by International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022 Oral Talk
专题命中 医学影像 :MRI(title,abstract);分类 cs.CV、cs.LG、eess.IV
Comments Code available at: https://github.com/HJ-harry/score-MRI
专题命中 医学影像 :MRI(title,abstract);分类 cs.CV、cs.LG、eess.IV;biomedical(journal_ref)
Comments 4 pages, 3 figures, 1 table, accepted in IEEE ISBI 2022
Journal ref [C]//2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE, 2022: 1-4
专题命中 医学影像 :medical image(title,abstract);分类 cs.CV、cs.LG、eess.IV
Comments Accept by MICCAI 2021, code at: https://github.com/jacobzhaoziyuan/MT-UDA
Journal ref Medical Image Computing and Computer Assisted Intervention, MICCAI 2021. Lecture Notes in Computer Science, vol 12901. Springer, Cham
专题命中 医学影像 :MRI(title,abstract);分类 cs.CV、cs.LG、eess.IV;diagnosis(comments)
Comments Accepted at IEEE ICCV-2021 workshop on Computer Vision for Automated Medical Diagnosis
专题命中 医学影像 :MRI(title,abstract);分类 cs.CV、cs.LG、eess.IV;biomedical(comments)
Comments 5 pages. Accepted as a full paper at the International Symposium on Biomedical Imaging (ISBI) 2021
专题命中 医学影像 :medical image(title,abstract);分类 cs.CV、cs.LG、eess.IV;biomedical(journal_ref)
Comments the winning model of the segmentation generalization challenge at EndoCV 2021
Journal ref Proceedings of the 3rd International Workshop and Challenge on Computer Vision in Endoscopy (EndoCV 2021) colocated with with the 17th IEEE International Symposium on Biomedical Imaging (ISBI 2021)
专题命中 医学影像 :MRI(title,abstract);分类 cs.CV、cs.LG、eess.IV;biomedical(comments)
Comments 5 pages, 2 figures. accepted for presentation at the International Symposium on Biomedical Imaging (ISBI) 2021. Code available at https://github.com/icometrix/gmm-augmentation
专题命中 医学影像 :medical image(title,abstract);分类 cs.CV、cs.LG、eess.IV;MRI(comments)
Comments 13 pages, accepted for publication in IEEE Transactions on Medical Imaging (TMI); Applied to Heart (Cardiac cine-MRI and Echocardiography), Prostate, and Inner ear image segmentation
专题命中 医学影像 :medical image(title,abstract);分类 cs.CV、cs.LG、eess.IV
Journal ref Medical Image Analysis, Volume 67, 2021, Medical Image Analysis, Volume 67,2021,101834,ISSN 1361-8415,
专题命中 医学影像 :radiology(title,abstract);medical image(abstract);biomedical(journal_ref)
Journal ref Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, ISBN 978-989-758-398-8, pages 178-186. 2020
专题命中 医学影像 :CT(title,abstract);分类 cs.CV、cs.LG、eess.IV
Comments 4 pages, 5 figures, 1 table. Accepted at CT Meeting 2020
专题命中 医学影像 :medical image(title,abstract);分类 cs.CV、cs.LG、eess.IV
Comments Accepted for publication in the journal of Medical Image Analysis
专题命中 医学影像 :CT(title,abstract);分类 cs.CV、cs.LG、eess.IV;medical image(comments)
Comments Accepted for oral presentation at the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2019
专题命中 医学影像 :medical image(title,abstract);分类 cs.CV、cs.LG、eess.IV
Comments MICCAI(International Conference on Medical Image Computing and Computer Assisted Intervention) 2019 accepted
专题命中 医学影像 :MRI(title,abstract);CT(abstract);radiology(comments)
Comments single pdf file in manuscript format, 35 pages, 5 figures Submitted to Academic Radiology
专题命中 医学影像 :CT(title,abstract);medical image(abstract);radiology(journal_ref)
Journal ref International Journal of Computer Assisted Radiology and Surgery 2, 1 (2007) 31-41
矩核:深度卷积网络中应对旋转与反射等变的简单可扩展方法
机构 * Department of Computational Medicine University of California, Los Angeles(计算医学系 加州大学洛杉矶分校)
专题命中 医学影像 :MRI(abstract,abstract_cn);medical image(abstract);biomedical(abstract);分类 cs.CV、cs.LG
AI总结 本文提出矩核,一种简单可扩展的正交变换等变卷积核,在生物医学图像任务中提升方向一致性,避免群卷积的方向通道扩展,适配标准CNN工作流程。
用于高效患者特异性术中二维/三维配准的患者无关合成预训练
专题命中 医学影像 :CT(summary_cn,abstract);分类 cs.CV、eess.IV
AI总结 研究术中二维/三维配准问题,提出基于患者无关合成预训练和球面相似性学习的框架,先预训练模型学习可转移表示,再用目标CT投影适应新患者,引入无分割域随机化策略,实验证明该方法能降低训练需求并保持配准精度。
用于引入未见医学成像模态的可转移低秩卷积基
机构 * Bangladesh University of Engineering and Technology (BUET)(孟加拉国工程技术大学) ; Osaka Metropolitan University(大阪都市大学) ; Chittagong University of Engineering and Technology (CUET)(吉大港工程技术大学) ; Kochi University of Technology(高知工科大学)
专题命中 医学影像 :MRI(abstract,abstract_cn);CT(abstract,abstract_cn);分类 cs.CV、cs.LG
AI总结 研究医学成像模型引入未见模态的问题,提出冻结源模态低秩卷积基并训练其向上投影的方法,该方法可转移,能以极少参数引入未见模态,保持源模态准确率不变,还能检测何时需引入。
使用深度学习对胰腺癌可切除性进行多模态评估
机构 * University of Basel(巴塞尔大学) ; Clarunis, University Digestive Health Centre(克拉鲁尼斯大学消化健康中心) ; Kantonsspital Aarau(阿劳州立医院) ; Royal Free Hospital(皇家自由医院)
专题命中 医学影像 :CT(summary_cn,abstract);分类 cs.CV、cs.LG
AI总结 研究利用深度学习框架联合分析CT与临床信息,对胰腺癌可切除性分类。通过Swin-UNETR主干获取图像特征,融合临床嵌入,经动态多任务目标训练,能将患者分入NCCN的三种可切除性类别,提高评估准确性。
基于深度活动轮廓和平均曲率损失函数的医学图像分割
专题命中 医学影像 :medical image(title,abstract);分类 cs.CV、eess.IV;biomedical(comments)
AI总结 针对医学图像分割中像素级训练缺乏几何先验信息及分割区域表征不足的问题,提出深度活动轮廓和平均曲率(DACMC)损失函数,用卷积核近似平均曲率,在多数据集上验证性能,展现新最优表现。
Comments 15 pages, 4 figures. Keywords: medical image segmentation, curvature regularization, loss function, active contour model, mean curvature, deep learning. Under review at Biomedical Signal Processing and Control
基于高效扩散变压器的PNI预测的自适应路由
机构 * Seoul National University(首尔国立大学) ; Kyung Hee University(庆熙大学) ; OUTTA ; Chung-Ang University(Chung-Ang 大学) ; Seoul National University School of Medicine(首尔国立大学医学院) ; Samsung Changwon Hospital(三星昌原医院) ; Samsung Medical Center(三星医疗中心) ; NVIDIA AI Technology Center(NVIDIA AI 技术中心)
专题命中 医学影像 :MRI(summary_cn,abstract);分类 cs.CV、cs.LG
AI总结 针对胆管癌PNI术前MRI预测难题,传统方法有局限。本文将PNI预测设为扩散分类问题,用基于Transformer的表示实现去噪网络,并引入自适应路由提高效率,实验取得了0.731的AUC及257.57 GFLOPs的结果。