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Thomas Brox

Showing results (41-50 of 45) with videos related to

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Development (Cambridge, England)|November 5, 2021
Deep learning is widely applicable to phenotyping embryonic development and diseaseThomas Naert, Özgün Çiçek, Paulina Ogar, et al.
Proceedings of the National Academy of Sciences of the United States of America|November 10, 2010
Inversin relays Frizzled-8 signals to promote proximal pronephros developmentSoeren Lienkamp, Athina Ganner, Christopher Boehlke, et al.
Nature Methods|February 27, 2019
Author Correction: U-Net: deep learning for cell counting, detection, and morphometryThorsten Falk, Dominic Mai, Robert Bensch, et al.
Nature Methods|December 19, 2018
U-Net: deep learning for cell counting, detection, and morphometryThorsten Falk, Dominic Mai, Robert Bensch, et al.
Nature Methods|October 31, 2017
An objective comparison of cell-tracking algorithmsVladimír Ulman, Martin Maška, Klas E G Magnusson, et al.
Pageof 5

Showing results (41-50 of 45) with videos related to

Sort By:
Pageof 5
You have reached the last page of results.This site can display upto 45 results.
Development (Cambridge, England)|November 5, 2021
Deep learning is widely applicable to phenotyping embryonic development and diseaseThomas Naert, Özgün Çiçek, Paulina Ogar, et al.
Proceedings of the National Academy of Sciences of the United States of America|November 10, 2010
Inversin relays Frizzled-8 signals to promote proximal pronephros developmentSoeren Lienkamp, Athina Ganner, Christopher Boehlke, et al.
Nature Methods|February 27, 2019
Author Correction: U-Net: deep learning for cell counting, detection, and morphometryThorsten Falk, Dominic Mai, Robert Bensch, et al.
Nature Methods|December 19, 2018
U-Net: deep learning for cell counting, detection, and morphometryThorsten Falk, Dominic Mai, Robert Bensch, et al.
Nature Methods|October 31, 2017
An objective comparison of cell-tracking algorithmsVladimír Ulman, Martin Maška, Klas E G Magnusson, et al.
Pageof 5