Items where authors include "Vesal, S"

Article

Zhuang, X, Xu, J, Luo, X et al. (20 more authors) (2022) Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge. Medical Image Analysis, 81. 102528. ISSN 1361-8415

Vesal, S, Gu, M, Kosti, R et al. (2 more authors) (2021) Adapt Everywhere: Unsupervised Adaptation of Point-Clouds and Entropy Minimisation for Multi-modal Cardiac Image Segmentation. IEEE Transactions on Medical Imaging. ISSN 0278-0062

Xiong, Z, Xia, Q, Hu, Z et al. (41 more authors) (2021) A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging. Medical Image Analysis, 67. 101832. ISSN 1361-8415

Vesal, S, Gu, M, Maier, A et al. (1 more author) (2020) Spatio-temporal Multi-task Learning for Cardiac MRI Left Ventricle Quantification. IEEE Journal of Biomedical and Health Informatics. ISSN 2168-2194

Vesal, S, Maier, A and Ravikumar, N (2020) Fully Automated 3D Cardiac MRI Localisation and Segmentation Using Deep Neural Networks. Journal of Imaging, 6 (7). 65. ISSN 2313-433X

Proceedings Paper

Hatamian, FN, Ravikumar, N, Vesal, S et al. (3 more authors) (2020) The Effect of Data Augmentation on Classification of Atrial Fibrillation in Short Single-Lead ECG Signals Using Deep Neural Networks. In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 04-08 May 2020, Barcelona, Spain. IEEE , pp. 1264-1268. ISBN 978-1-5090-6632-2

Vesal, S, Ravikumar, N and Maier, A (2020) Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation. In: Lecture Notes in Computer Science. STACOM 2019: Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges, 12 Oct 2019, Shenzen, China. Springer Verlag , pp. 300-308. ISBN 9783030390730

Kaiser, N, Fieselmann, A, Vesal, S et al. (4 more authors) (2019) Mammographic breast density classification using a deep neural network: assessment based on inter-observer variability. In: Nishikawa, RM and Samuelson, FW, (eds.) Proceedings of SPIE: Progress in Biomedical Optics and Imaging. SPIE Medical Imaging 2019, 16-21 Feb 2019, San Diego, CA, United States. SPIE . ISBN 9781510625518

Vesal, S, Ravikumar, N and Maier, A (2019) A 2D dilated residual U-net for multi-organ segmentation in thoracic CT. In: Petitjean, C, Ruan, S, Lambert, Z and Dubray, B, (eds.) CEUR Workshop Proceedings. SegTHOR2019: 2019 Challenge on Segmentation of THoracic Organs at Risk in CT Images, 08-11 Apr 2019, Venice, Italy. CEUR Workshop Proceedings .

Vesal, S, Ravikumar, N and Maier, A (2019) Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI. In: Pop, M, Sermesant, M, Zhao, J, Li, S, McLeod, K, Young, A, Rhode, K and Mansi, T, (eds.) Lecture Notes in Computer Science. STACOM 2018: 9th Statistical Atlases and Computational Modelling of the Heart Workshop, 16 Sep 2018, Granada, Spain. Springer Verlag , pp. 319-328. ISBN 978-3-030-12028-3

Folle, L, Vesal, S, Ravikumar, N et al. (1 more author) (2019) Dilated Deeply Supervised Networks for Hippocampus Segmentation in MRI. In: Handels, H, Deserno, TM, Maier, A, Maier-Hein, KH, Palm, C and Tolxdorff, T, (eds.) Informatik aktuell. BVM 2019: Bildverarbeitung für die Medizin, 17-19 Mar 2019, Lübeck, Germany. Springer Vieweg , pp. 68-73. ISBN 978-3-658-25325-7