Browse by UCL people
Group by: Type | Date
Number of items: 20.
Article
Alexander, DC;
Zikic, D;
Ghosh, A;
Tanno, R;
Wottschel, V;
Zhang, J;
Kaden, E;
... Criminisi, A; + view all
(2017)
Image quality transfer and applications in diffusion MRI.
NeuroImage
, 152
pp. 283-298.
10.1016/j.neuroimage.2017.02.089.
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Quan, K;
Tanno, R;
Shipley, RJ;
Brown, JS;
Jacob, J;
Hurst, JR;
Hawkes, DJ;
(2019)
Reproducibility of an airway tapering measurement in computed tomography with application to bronchiectasis.
Journal of Medical Imaging
, 6
(3)
, Article 034003. 10.1117/1.JMI.6.3.034003.
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Sudre, CH;
Anson, BG;
Ingala, S;
Lane, CD;
Jimenez, D;
Haider, L;
Varsavsky, T;
... Cardoso, MJ; + view all
(2019)
Let's Agree to Disagree: Learning Highly Debatable Multirater Labelling.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
, 11767
pp. 665-673.
10.1007/978-3-030-32251-9_73.
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Tanno, R;
Worrall, DE;
Kaden, E;
Ghosh, A;
Grussu, F;
Bizzi, A;
Sotiropoulos, SN;
... Alexander, DC; + view all
(2021)
Uncertainty modelling in deep learning for safer neuroimage enhancement: Demonstration in diffusion MRI.
NeuroImage
, 225
, Article 117366. 10.1016/j.neuroimage.2020.117366.
(In press).
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Tax, CMW;
Grussu, F;
Kaden, E;
Ning, L;
Rudrapatna, U;
Evans, CJ;
St-Jean, S;
... Veraart, J; + view all
(2019)
Cross-scanner and cross-protocol diffusion MRI data harmonisation: A benchmark database and evaluation of algorithms.
NeuroImage
, 195
pp. 285-299.
10.1016/j.neuroimage.2019.01.077.
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Zhang, L;
Tanno, R;
Xu, MC;
Jin, C;
Jacob, J;
Ciccarelli, O;
Barkhof, F;
(2020)
Disentangling human error from the ground truth in segmentation of medical images.
Advances in Neural Information Processing Systems
, 2020-D
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Proceedings paper
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Blumberg, SB;
Palombo, M;
Khoo, CS;
Tax, CMW;
Tanno, R;
Alexander, DC;
(2019)
Multi-stage prediction networks for data harmonization.
In: Shen, D and Liu, T and Peters, TM and Staib, LH and Essert, C and Zhou, S and Yap, P-T and Khan, A, (eds.)
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019.
(pp. pp. 411-419).
Springer: Shenzhen, China.
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Bragman, F;
Tanno, R;
Ourselin, S;
Alexander, D;
Cardoso, J;
(2020)
Stochastic filter groups for multi-task cnns: Learning specialist and generalist convolution kernels.
In:
Proceedings of 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
(pp. pp. 1385-1394).
IEEE: Seoul, Korea.
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Bragman, FJS;
Tanno, R;
Eaton-Rosen, Z;
Li, W;
Hawkes, DJ;
Ourselin, S;
Alexander, DC;
... Cardoso, MJ; + view all
(2018)
Uncertainty in Multitask Learning: Joint Representations for Probabilistic MR-only Radiotherapy Planning.
In: Frangi, AF and Schnabel, JA and Davatzikos, C and Alberola-López, C and Fichtinger, G, (eds.)
Medical Image Computing and Computer Assisted Intervention: Proceedings, Part IV.
(pp. pp. 3-11).
Springer: Cham, Switzerland.
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Bragman, FJS;
Tanno, R;
Ourselin, S;
Alexander, DC;
Cardoso, MJ;
(2019)
Learning task-specific and shared representations in medical imaging.
In:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019.
(pp. pp. 374-383).
Springer Nature: Cham, Switzerland.
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Jin, C;
Tanno, R;
Xu, M;
Mertzanidou, T;
Alexander, DC;
(2020)
Foveation for Segmentation of Mega-Pixel Histology Images.
In:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020.
(pp. pp. 561-571).
Springer Nature: Cham, Switzerland.
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Lin, H;
Figini, M;
Tanno, R;
Blumberg, SB;
Kaden, E;
Ogbole, G;
Brown, BJ;
... Alexander, DC; + view all
(2019)
Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator.
In:
Machine Learning for Medical Image Reconstruction.
(pp. pp. 58-70).
Springer Nature: Cham, Switzerland.
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Quan, K;
Shipley, R;
Tanno, R;
McPhillips, G;
Vavourakis, V;
Edwards, D;
Jacob, J;
... Hawkes, D; + view all
(2018)
Tapering Analysis of Airways with Bronchiectasis.
In:
Proceedings of SPIE 10574, Medical Imaging 2018: Image Processing, 105742G.
(pp. 105742G).
SPIE: Texas, USA.
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Quan, K;
Tanno, R;
Duong, M;
Nair, A;
Shipley, R;
Jones, M;
Brereton, C;
... Jacob, J; + view all
(2019)
Modelling Airway Geometry as Stock Market Data Using Bayesian Changepoint Detection.
In: Suk, H and Liu, M and Yan, P and Lian, C, (eds.)
MLMI 2019: Machine Learning in Medical Imaging.
(pp. pp. 345-354).
Springer: Shenzhen, China.
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Tanno, R;
Arulkumaran, K;
Alexander, DC;
Criminisi, A;
Nori, A;
(2019)
Adaptive neural trees.
In:
Proceedings of the 36th International Conference on Machine Learning.
(pp. pp. 6166-6175).
Proceedings of Machine Learning Research
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Tanno, R;
Ghosh, A;
Grussu, F;
Kaden, E;
Criminisi, A;
Alexander, DC;
(2016)
Bayesian image quality transfer.
In:
Medical Image Computing and Computer-Assisted Intervention. MICCAI 2016:19th International Conference. Proceedings, Part II.
(pp. pp. 265-273).
Springer International Publishing
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Tanno, R;
Saeedi, A;
Sankaranarayanan, S;
Alexander, DC;
Silberman, N;
(2020)
Learning From Noisy Labels by Regularized Estimation of Annotator Confusion.
In:
Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
(pp. pp. 11236-11245).
IEEE: Long Beach, CA, USA.
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Tanno, R;
Worrall, DE;
Ghosh, A;
Kaden, E;
Sotiropoulos, SN;
Criminisi, A;
Alexander, DC;
(2017)
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution.
In: Descoteaux, M and Maier-Hein, L and Franz, A and Jannin, P and Collins, D and Duchesne, S, (eds.)
MICCAI 2017: 20th International Conference, Medical Image Computing and Computer Assisted Intervention: Proceedings, Part I.
(pp. pp. 611-619).
Springer International Publishing: Cham, Switzerland.
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Zhang, L;
Tanno, R;
Bronik, K;
Jin, C;
Nachev, P;
Barkhof, F;
Ciccarelli, O;
(2020)
Learning to segment when experts disagree.
In:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020.
(pp. pp. 179-190).
Springer: Peru.
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Thesis
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Tanno, Ryutaro;
(2021)
Reasoning with Uncertainty in Deep Learning for Safer Medical Image Computing.
Doctoral thesis (Ph.D), UCL (University College London).
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