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  • Currently displaying 81 - 100 of 313 publications
Improving “Fast Iterative Shrinkage-Thresholding Algorithm”: Faster, Smarter, and Greedier
J Liang, T Luo, CB Schonlieb
– SIAM Journal on Scientific Computing
(2022)
44,
A1069
(doi: 10.1137/21M1395685)
Learning Green's functions associated with time-dependent partial differential equations
N Boullé, S Kim, T Shi, A Townsend
(2022)
(link to publication)
Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence (vol 3, pg 1081, 2021)
X Bai, H Wang, L Ma, Y Xu, J Gan, Z Fan, F Yang, K Ma, J Yang, S Bai, C Shu, X Zou, R Huang, C Zhang, X Liu, D Tu, C Xu, W Zhang, X Wang, A Chen, Y Zeng, D Yang, M-W Wang, N Holalkere, NJ Halin, IR Kamel, J Wu, X Peng, X Wang, J Shao, P Mongkolwat, J Zhang, W Liu, M Roberts, Z Teng, L Beer, LE Sanchez, E Sala, DL Rubin, A Weller, J Lasenby, C Zheng, J Wang, Z Li, C Schönlieb, T Xia
– Nature Machine Intelligence
(2022)
4,
413
(doi: 10.1038/s42256-022-00485-5)
Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification
AI Aviles-Rivero, C Runkel, N Papadakis, Z Kourtzi, C-B Schönlieb
– MICCAI 2022
(2022)
(link to publication)
Comparative Performance of Fully-Automated and Semi-Automated Artificial Intelligence Methods for the Detection of Clinically Significant Prostate Cancer on MRI: a Systematic Review
N Sushentsev, N Moreira Da Silva, M Yeung, T Barrett, E Sala, M Roberts, L Rundo
– Insights into Imaging
(2022)
13,
59
(doi: 10.1186/s13244-022-01199-3)
Comparative performance of fully-automated and semi-automated artificial intelligence methods for the detection of clinically significant prostate cancer on MRI: a systematic review
N Sushentsev, N Moreira Da Silva, M Yeung, T Barrett, E Sala, M Roberts, L Rundo
– Insights into Imaging
(2022)
13,
59
(doi: 10.1186/s13244-022-01199-3)
Data-driven discovery of Green’s functions with human-understandable deep learning
N Boullé, CJ Earls, A Townsend
– Scientific Reports
(2022)
12,
4824
(doi: 10.1038/s41598-022-08745-5)
A Three-Stage Self-Training Framework for Semi-Supervised Semantic Segmentation
R Ke, AI Aviles-Rivero, S Pandey, S Reddy, C-B Schonlieb
– IEEE Trans Image Process
(2022)
31,
1805
(doi: 10.1109/TIP.2022.3144036)
AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis
Y Chen, CB Schonlieb, P Lio, T Leiner, PL Dragotti, G Wang, D Rueckert, D Firmin, G Yang
– Proceedings of the IEEE
(2022)
110,
224
(doi: 10.1109/JPROC.2022.3141367)
Optimization of Hopf bifurcation points
N Boullé, PE Farrell, ME Rognes
(2022)
(link to publication)
Learning Elliptic Partial Differential Equations with Randomized Linear Algebra
N Boullé, A Townsend
– Foundations of Computational Mathematics
(2022)
23,
709
(doi: 10.1007/s10208-022-09556-w)
Control of Bifurcation Structures using Shape Optimization
N Boullé, PE Farrell, A Paganini
– SIAM Journal on Scientific Computing
(2022)
44,
a57
(doi: 10.1137/21m1418708)
Preface
G Yang, A Aviles-Rivero, M Roberts, CB Schönlieb
– Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
(2022)
13413 LNCS,
v
AI and Point of Care Image Analysis for COVID-19
M Roberts, O Frank, S Bagon, YC Eldar, CB Schönlieb
(2022)
85
(doi: 10.1007/978-3-031-08506-2_4)
On Biased Stochastic Gradient Estimation
D Driggs, J Liang, CB Schönlieb
– Journal of Machine Learning Research
(2022)
23,
Medical Image Understanding and Analysis
(2022)
(doi: 10.1007/978-3-031-12053-4)
TFPnP: Tuning-free Plug-and-Play Proximal Algorithms with Applications to Inverse Imaging Problems
K Wei, A Aviles-Rivero, J Liang, Y Fu, H Huang, CB Schonlieb
– Journal of Machine Learning Research
(2022)
23,
ARTN 1
(link to publication)
Estimation of the zero-pressure computational start shape of atherosclerotic plaques: Improving the backward displacement method with deformation gradient tensor.
Y Huang, S Wang, T Luo, MH-F Du, C Sun, U Sadat, C-B Schönlieb, JH Gillard, J Zhang, Z Teng
– Journal of Biomechanics
(2021)
131,
110910
(doi: 10.1016/j.jbiomech.2021.110910)
Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence
X Bai, H Wang, L Ma, Y Xu, J Gan, Z Fan, F Yang, K Ma, J Yang, S Bai, C Shu, X Zou, R Huang, C Zhang, X Liu, D Tu, C Xu, W Zhang, X Wang, A Chen, Y Zeng, D Yang, M-W Wang, N Holalkere, NJ Halin, IR Kamel, J Wu, X Peng, X Wang, J Shao, P Mongkolwat, J Zhang, W Liu, M Roberts, Z Teng, L Beer, L Escudero Sanchez, E Sala, D Rubin, A Weller, J Lasenby, C Zheng, J Wang, Z Li, C-B Schönlieb, T Xia
– ArXiv
(2021)
3,
1081
(doi: 10.1038/s42256-021-00421-z)
Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence.
X Bai, H Wang, L Ma, Y Xu, J Gan, Z Fan, F Yang, K Ma, J Yang, S Bai, C Shu, X Zou, R Huang, C Zhang, X Liu, D Tu, C Xu, W Zhang, X Wang, A Chen, Y Zeng, D Yang, M-W Wang, N Holalkere, NJ Halin, IR Kamel, J Wu, X Peng, X Wang, J Shao, P Mongkolwat, J Zhang, W Liu, M Roberts, Z Teng, L Beer, LE Sanchez, E Sala, DL Rubin, A Weller, J Lasenby, C Zheng, J Wang, Z Li, C Schönlieb, T Xia
– Nature machine intelligence
(2021)
3,
1081
(doi: 10.1038/s42256-021-00421-z)

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