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ORCIDMedical Image Analysis, Volume 61
Volume 61, April 2020
- Cesare Corrado
, Orod Razeghi, Caroline H. Roney, Sam Coveney, Steven Williams, Iain Sim, Mark D. O'Neill, Richard Wilkinson, Jeremy E. Oakley, Richard H. Clayton, Steven A. Niederer:
Quantifying atrial anatomy uncertainty from clinical data and its impact on electro-physiology simulation predictions. 101626 - Baiying Lei, Yujia Zhao, Zhongwei Huang, Xiaoke Hao, Feng Zhou, Ahmed Elazab, Jing Qin, Haijun Lei:
Adaptive sparse learning using multi-template for neurodegenerative disease diagnosis. 101632 - Hua Ma, Ihor Smal, Joost Daemen, Theo van Walsum:
Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering. 101634 - Jian Li, Soyoung Choi, Anand A. Joshi, Jessica L. Wisnowski, Richard M. Leahy:
Temporal non-local means filtering for studies of intrinsic brain connectivity from individual resting fMRI. 101635 - Fumin Guo, Matthew Ng, Maged Goubran, Steffen E. Petersen, Stefan K. Piechnik, Stefan Neubauer, Graham A. Wright:
Improving cardiac MRI convolutional neural network segmentation on small training datasets and dataset shift: A continuous kernel cut approach. 101636 - Jie Liu, Giulio Gambarota, Huazhong Shu, Longyu Jiang, Benjamin Leporq, Olivier Beuf, Ahmad Karfoul:
On the identification of the blood vessel confounding effect in intravoxel incoherent motion (IVIM) Diffusion-Weighted (DW)-MRI in liver: An efficient sparsity based algorithm. 101637 - Suyu Dong, Gongning Luo, Clara M. Tam, Wei Wang, Kuanquan Wang, Shaodong Cao, Bo Chen, Henggui Zhang, Shuo Li:
Deep Atlas Network for Efficient 3D Left Ventricle Segmentation on Echocardiography. 101638 - Benjamin Thyreau, Yasuyuki Taki:
Learning a cortical parcellation of the brain robust to the MRI segmentation with convolutional neural networks. 101639 - Chuyang Ye, Yuxing Li, Xiangzhu Zeng:
An improved deep network for tissue microstructure estimation with uncertainty quantification. 101650 - Baiying Lei, Nina Cheng, Alejandro F. Frangi, Ee-Leng Tan, Jiuwen Cao, Peng Yang, Ahmed Elazab, Jie Du, Yanwu Xu, Tianfu Wang:
Self-calibrated brain network estimation and joint non-convex multi-task learning for identification of early Alzheimer's disease. 101652 - Yaxin Shen, Bin Sheng, Ruogu Fang, Huating Li, Ling Dai, Skylar E. Stolte, Jing Qin, Weiping Jia, Dinggang Shen:
Domain-invariant interpretable fundus image quality assessment. 101654 - Tanja Lossau, Hannes Nickisch, Tobias Wissel, Michael M. Morlock, Michael Grass:
Learning metal artifact reduction in cardiac CT images with moving pacemakers. 101655 - Lei Du, Kefei Liu, Xiaohui Yao, Shannon L. Risacher, Junwei Han, Andrew J. Saykin, Lei Guo, Li Shen:
Detecting genetic associations with brain imaging phenotypes in Alzheimer's disease via a novel structured SCCA approach. 101656 - Qinghua Huang, Yonghao Huang, Yaozhong Luo, Feiniu Yuan, Xuelong Li:
Segmentation of breast ultrasound image with semantic classification of superpixels. 101657 - Dongqing Zhang, Jianing Wang, Jack H. Noble, Benoit M. Dawant:
HeadLocNet: Deep convolutional neural networks for accurate classification and multi-landmark localization of head CTs. 101659 - Gwenolé Quellec, Mathieu Lamard, Pierre-Henri Conze, Pascale Massin, Béatrice Cochener:
Automatic detection of rare pathologies in fundus photographs using few-shot learning. 101660 - Shiv Gehlot, Anubha Gupta, Ritu Gupta:
SDCT-AuxNetθ: DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis. 101661 - Lituan Wang, Lei Zhang, Minjuan Zhu, Xiaofeng Qi, Zhang Yi:
Automatic diagnosis for thyroid nodules in ultrasound images by deep neural networks. 101665 - Tao He, Junjie Hu, Ying Song, Jixiang Guo, Zhang Yi:
Multi-task learning for the segmentation of organs at risk with label dependence. 101666
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