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Multi-task Learning for Chest X-ray Abnormality Classification on Noisy Labels.

S. Gündel, F. Ghesu, S. Grbic, E. Gibson, B. Georgescu, A. Maier, and D. Comaniciu. CoRR, (2019)

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Prof. Dr. -Ing. Rudolf Eligehausen University of Stuttgart

Other publications of authors with the same name

Designing image segmentation studies: Statistical power, sample size and reference standard quality.E. Gibson, Y. Hu, H. Huisman, and D. Barratt. Medical Image Analysis, (2017)Registration Accuracy: How Good Is Good Enough? A Statistical Power Calculation Incorporating Image Registration Uncertainty.E. Gibson, A. Fenster, and A. Ward. MICCAI (2), volume 7511 of Lecture Notes in Computer Science, page 643-650. Springer, (2012)2D-3D Registration Accuracy Estimation for Optimised Planning of Image-Guided Pancreatobiliary Interventions.Y. Hu, E. Bonmati, E. Gibson, J. Hipwell, D. Hawkes, S. Bandula, S. Pereira, and D. Barratt. MICCAI (1), volume 9900 of Lecture Notes in Computer Science, page 516-524. (2016)Inter-site Variability in Prostate Segmentation Accuracy Using Deep Learning.E. Gibson, Y. Hu, N. Ghavami, H. Ahmed, C. Moore, M. Emberton, H. Huisman, and D. Barratt. MICCAI (4), volume 11073 of Lecture Notes in Computer Science, page 506-514. Springer, (2018)Multiparametric MR imaging of prostate cancer foci: assessing the detectability and localizability of Gleason 7 peripheral zone cancers based on image contrasts.E. Gibson, M. Gaed, W. Hrinivich, J. Gómez, M. Moussa, C. Romagnoli, J. Mandel, M. Bastian-Jordan, D. Cool, S. Ghoul and 6 other author(s). Medical Imaging: Digital Pathology, volume 9041 of SPIE Proceedings, page 90410N. SPIE, (2014)Toward quantitative digital histopathology for prostate cancer: comparison of inter-slide interpolation methods for tumour measurement.M. Salarian, M. Shahedi, E. Gibson, M. Gaed, J. Gómez, M. Moussa, G. Bauman, and A. Ward. Medical Imaging: Digital Pathology, volume 8676 of SPIE Proceedings, page 86760F. SPIE, (2013)Label-driven weakly-supervised learning for multimodal deformarle image registration.Y. Hu, M. Modat, E. Gibson, N. Ghavami, E. Bonmati, C. Moore, M. Emberton, J. Noble, D. Barratt, and T. Vercauteren. ISBI, page 1070-1074. IEEE, (2018)Tissue block MRI for slice orientation-independent registration of digital histology images to ex vivo MRI of the prostate.E. Gibson, C. Crukley, J. Gomez, M. Moussa, G. Bauman, A. Fenster, and A. Ward. ISBI, page 566-569. IEEE, (2011)Automatic segmentation of prostate MRI using convolutional neural networks: Investigating the impact of network architecture on the accuracy of volume measurement and MRI-ultrasound registration.N. Ghavami, Y. Hu, E. Gibson, E. Bonmati, M. Emberton, C. Moore, and D. Barratt. Medical Image Analysis, (2019)NiftyNet: a deep-learning platform for medical imaging.E. Gibson, W. Li, C. Sudre, L. Fidon, D. Shakir, G. Wang, Z. Eaton-Rosen, R. Gray, T. Doel, Y. Hu and 6 other author(s). CoRR, (2017)

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Multi-task Learning for Chest X-ray Abnormality Classification on Noisy Labels.

Please choose a person to relate this publication to

Prof. Dr. -Ing. Rudolf Eligehausen University of Stuttgart

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Disambiguation of "Gibson, Eli"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Multi-task Learning for Chest X-ray Abnormality Classification on Noisy Labels.

Please choose a person to relate this publication to

Prof. Dr. -Ing. Rudolf Eligehausen University of Stuttgart

Other publications of authors with the same name

Disambiguation

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Multi-task Learning for Chest X-ray Abnormality Classification on Noisy Labels.