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Tal Arbel

Showing results (41-50 of 56) with videos related to

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IEEE Transactions on Medical Imaging|September 20, 2021
Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning InferenceRaghav Mehta, Thomas Christinck, Tanya Nair, et al.
Nature Communications|September 26, 2022
Estimating individual treatment effect on disability progression in multiple sclerosis using deep learningJean-Pierre R Falet, Joshua Durso-Finley, Brennan Nichyporuk, et al.
Journal of Medical Imaging (Bellingham, Wash.)|February 3, 2018
Combining intraoperative ultrasound brain shift correction and augmented reality visualizations: a pilot study of eight casesIan J Gerard, Marta Kersten-Oertel, Simon Drouin, et al.
Medical Image Analysis|September 10, 2020
BIAS: Transparent reporting of biomedical image analysis challengesLena Maier-Hein, Annika Reinke, Michal Kozubek, et al.
International Journal of Computer Assisted Radiology and Surgery|September 2, 2016
IBIS: an OR ready open-source platform for image-guided neurosurgerySimon Drouin, Anna Kochanowska, Marta Kersten-Oertel, et al.
Medical Image Analysis|February 19, 2016
Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR imagesRashed Karim, Pranav Bhagirath, Piet Claus, et al.
Scientific Reports|May 20, 2020
Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice AnalysisAaron Carass, Snehashis Roy, Adrian Gherman, et al.
Nature Communications|February 1, 2019
Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with careLena Maier-Hein, Matthias Eisenmann, Annika Reinke, et al.
Nature Communications|December 8, 2018
Why rankings of biomedical image analysis competitions should be interpreted with careLena Maier-Hein, Matthias Eisenmann, Annika Reinke, et al.
Neuroimage|January 15, 2017
Longitudinal multiple sclerosis lesion segmentation: Resource and challengeAaron Carass, Snehashis Roy, Amod Jog, et al.
Pageof 6

Showing results (41-50 of 56) with videos related to

Sort By:
Pageof 6
IEEE Transactions on Medical Imaging|September 20, 2021
Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning InferenceRaghav Mehta, Thomas Christinck, Tanya Nair, et al.
Nature Communications|September 26, 2022
Estimating individual treatment effect on disability progression in multiple sclerosis using deep learningJean-Pierre R Falet, Joshua Durso-Finley, Brennan Nichyporuk, et al.
Journal of Medical Imaging (Bellingham, Wash.)|February 3, 2018
Combining intraoperative ultrasound brain shift correction and augmented reality visualizations: a pilot study of eight casesIan J Gerard, Marta Kersten-Oertel, Simon Drouin, et al.
Medical Image Analysis|September 10, 2020
BIAS: Transparent reporting of biomedical image analysis challengesLena Maier-Hein, Annika Reinke, Michal Kozubek, et al.
International Journal of Computer Assisted Radiology and Surgery|September 2, 2016
IBIS: an OR ready open-source platform for image-guided neurosurgerySimon Drouin, Anna Kochanowska, Marta Kersten-Oertel, et al.
Medical Image Analysis|February 19, 2016
Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR imagesRashed Karim, Pranav Bhagirath, Piet Claus, et al.
Scientific Reports|May 20, 2020
Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice AnalysisAaron Carass, Snehashis Roy, Adrian Gherman, et al.
Nature Communications|February 1, 2019
Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with careLena Maier-Hein, Matthias Eisenmann, Annika Reinke, et al.
Nature Communications|December 8, 2018
Why rankings of biomedical image analysis competitions should be interpreted with careLena Maier-Hein, Matthias Eisenmann, Annika Reinke, et al.
Neuroimage|January 15, 2017
Longitudinal multiple sclerosis lesion segmentation: Resource and challengeAaron Carass, Snehashis Roy, Amod Jog, et al.
Pageof 6