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of 164
pro vyhledávání: '"Rajchl M"'
Akademický článek
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Autor:
Rajchl, M., Lee, M., Schrans, F., Davidson, A., Passerat-Palmbach, J., Tarroni, G., Alansary, A., Oktay, O., Kainz, B., Rueckert, D.
The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods. While typically established by a clinical expert rater, the increase in acquired imaging data renders traditional pixel-wise s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=core_ac_uk__::d0a0fadac102343cc99677e8df95bd8b
https://openaccess.city.ac.uk/id/eprint/23502/1/1606.01100v1.pdf
https://openaccess.city.ac.uk/id/eprint/23502/1/1606.01100v1.pdf
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
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Autor:
Bai, W, Sinclair, M, Tarroni, G, Oktay, O, Rajchl, M, Vaillant, G, Lee, AM, Aung, N, Lukaschuk, E, Sanghvi, MM, Zemrak, F, Fung, K, Paiva, JM, Carapella, V, Kim, YJ, Suzuki, H, Kainz, B, Matthews, PM, Petersen, SE, Piechnik, SK, Neubauer, S, Glocker, B, Rueckert, D
Publikováno v:
Journal of Cardiovascular Magnetic Resonance
Journal of Cardiovascular Magnetic Resonance, Vol 20, Iss 1, Pp 1-12 (2018)
Journal of Cardiovascular Magnetic Resonance, Vol 20, Iss 1, Pp 1-12 (2018)
Cardiovascular magnetic resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and
Recent advances in deep learning led to novel generative modeling techniques that achieve unprecedented quality in generated samples and performance in learning complex distributions in imaging data. These new models in medical image computing have i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1032::e495260049b4d0d29d902f75937b7c00
http://hdl.handle.net/10044/1/62941
http://hdl.handle.net/10044/1/62941
We present DLTK, a toolkit providing baseline implementations for efficient experimentation with deep learning methods on biomedical images. It builds on top of TensorFlow and its high modularity and easy-to-use examples allow for a low-threshold acc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1032::47594c5cddd4bdc75d3bf0e66006f58c
http://hdl.handle.net/10044/1/54699
http://hdl.handle.net/10044/1/54699
We interpret HyperNetworks within the framework of variational inference within implicit distributions. Our method, Bayes by Hypernet, is able to model a richer variational distribution than previous methods. Experiments show that it achieves compara
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1032::1504ed67555364eecb490e54299dbad9
http://hdl.handle.net/10044/1/54603
http://hdl.handle.net/10044/1/54603
Autor:
Bai, W, Sinclair, M, Tarroni, G, Oktay, O, Rajchl, M, Vaillant, G, Lee, AM, Aung, N, Lukaschuk, E, Sanghvi, MM, Zemrak, F, Fung, K, Paiva, JM, Carapella, V, Kim, YJ, Suzuki, H, Kainz, B, Matthews, PM, Petersen, SE, Piechnik, SK, Neubauer, S, Glocker, B, Rueckert, D
Cardiovascular magnetic resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1032::70518ada36ed2bfc42fe59a84b456cb2
http://hdl.handle.net/10044/1/54263
http://hdl.handle.net/10044/1/54263
Publikováno v:
Proceedings of SPIE; 1/13/2019, Vol. 10950, p1-7, 7p
Autor:
Oktay, O, Bai, W, Guerrero, R, Rajchl, M, De Marvao, A, O'Regan, D, Cook, S, Heinrich, M, Glocker, B, Rueckert, D
Accurate localization of anatomical landmarks is an important step in medical imaging, as it provides useful prior information for subsequent image analysis and acquisition methods. It is particularly useful for initialization of automatic image anal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1032::31ee5f92c7cf14c413cccaabf0c59053
http://hdl.handle.net/10044/1/38907
http://hdl.handle.net/10044/1/38907