Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Rens Janssens"'
Publikováno v:
Bioinformatics. 37:4548-4555
Motivation The identification and discovery of phenotypes from high content screening images is a challenging task. Earlier works use image analysis pipelines to extract biological features, supervised training methods or generate features with neura
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c26a1fdbae242d42c759699c7cb6cabf
https://doi.org/10.1093/bioinformatics/btab497
https://doi.org/10.1093/bioinformatics/btab497
Autor:
Slavica Dimitrieva, Rens Janssens, Gang Li, Artur Szalata, Raja Gopal, Chintan Parmar, Audrey Kauffmann, Eric Y. Durand
Cell lines and patient-derived xenografts are essential to cancer research, however, the results derived from such models often lack clinical translatability, as these models do not fully recapitulate the complex cancer biology. It is critically impo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::602ea281a44922a3ef822dd9f196a23e
https://doi.org/10.1101/2022.09.07.506964
https://doi.org/10.1101/2022.09.07.506964
Autor:
Mark-Anthony Bray, Antoine de Weck, Eric Durand, Jian Fang, Daniela Gabriel, Rens Janssens, Ioannis Moutsatsos, Stephan Spiegel, Xian Zhang
Recent development of novel methods based on deep neural networks has transformed how high-content microscopy cellular images are analyzed. Nonetheless, it is still a challenge to identify cellular phenotypic changes caused by chemical or genetic tre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::89c7a8e180099c0a14b92c66f240460a
https://doi.org/10.1101/2022.05.15.491989
https://doi.org/10.1101/2022.05.15.491989
Autor:
Rens Janssens, Guoyan Zheng
Publikováno v:
EPiC Series in Health Sciences.
We present a method to address the challenging problem of automatic segmentation of lumbar vertebrae from CT images acquired with varying fields of view. Our method is based on cascaded 3D Fully Convolutional Networks (FCNs) consisting of a localizat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e1c3214315601809ff3a6b6c5a943cc9
https://doi.org/10.29007/vt7v
https://doi.org/10.29007/vt7v
Publikováno v:
ISBI
We present a method to address the challenging problem of segmentation of lumbar vertebrae from CT images acquired with varying fields of view. Our method is based on cascaded 3D Fully Convolutional Networks (FCNs) consisting of a localization FCN an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0e4583e95960c9c5aa436da8ad1d28ab