Zobrazeno 1 - 10
of 126
pro vyhledávání: '"Sailesh Conjeti"'
Publikováno v:
IEEE Access, Vol 8, Pp 213502-213511 (2020)
Histopathological Whole Slide Imaging (WSI) has become a standard in the detection of breast cancer. Automated image analysis methods attempt to reduce the workload from the clinicians and Convolutional Neural Networks (CNNs) are a popular choice for
Externí odkaz:
https://doaj.org/article/72adafb5f3db4a79a4d94177a06f0e13
Autor:
Leonie Henschel, Sailesh Conjeti, Santiago Estrada, Kersten Diers, Bruce Fischl, Martin Reuter
Publikováno v:
NeuroImage, Vol 219, Iss , Pp 117012- (2020)
Traditional neuroimage analysis pipelines involve computationally intensive, time-consuming optimization steps, and thus, do not scale well to large cohort studies with thousands or tens of thousands of individuals. In this work we propose a fast and
Externí odkaz:
https://doaj.org/article/26c3668f216c4845b8bae98311892577
Publikováno v:
F1000Research, Vol 5 (2017)
Ensemble methods have been successfully applied in a wide range of scenarios, including survival analysis. However, most ensemble models for survival analysis consist of models that all optimize the same loss function and do not fully utilize the div
Externí odkaz:
https://doaj.org/article/914d76ca16a2400d82a19eac3bf2f953
Publikováno v:
F1000Research, Vol 5 (2017)
Ensemble methods have been successfully applied in a wide range of scenarios, including survival analysis. However, most ensemble models for survival analysis consist of models that all optimize the same loss function and do not fully utilize the div
Externí odkaz:
https://doaj.org/article/dcf41360dec64edb8767a9c7edc0c96c
Publikováno v:
IEEE Access, Vol 8, Pp 213502-213511 (2020)
Histopathological Whole Slide Imaging (WSI) has become a standard in the detection of breast cancer. Automated image analysis methods attempt to reduce the workload from the clinicians and Convolutional Neural Networks (CNNs) are a popular choice for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7825ddde9283c99bdbc2131656477658
https://doi.org/10.1109/access.2020.3040106
https://doi.org/10.1109/access.2020.3040106
Autor:
Fatemeh Homayounieh, Subba Digumarthy, Shadi Ebrahimian, Johannes Rueckel, Boj Friedrich Hoppe, Bastian Oliver Sabel, Sailesh Conjeti, Karsten Ridder, Markus Sistermanns, Lei Wang, Alexander Preuhs, Florin Ghesu, Awais Mansoor, Mateen Moghbel, Ariel Botwin, Ramandeep Singh, Samuel Cartmell, John Patti, Christian Huemmer, Andreas Fieselmann, Clemens Joerger, Negar Mirshahzadeh, Victorine Muse, Mannudeep Kalra
Publikováno v:
JAMA Network Open
Key Points Question Can artificial intelligence (AI) improve detection of pulmonary nodules on chest radiographs at different levels of detection difficulty? Findings In this diagnostic study, AI-aided interpretation was associated with significantly
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::89595635ddf6c5207ecbc7a01a0132de
http://europepmc.org/articles/PMC8717119
http://europepmc.org/articles/PMC8717119
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 7, Iss 5 (2014)
Designing a wearable driver assist system requires extraction of relevant features from physiological signals like galvanic skin response and photoplethysmogram collected from automotive drivers during real-time driving. In the discussed case, four s
Externí odkaz:
https://doaj.org/article/66dd22408c004667aca6d1a6035f6d8a
STRATEGY FOR ELECTROMYOGRAPHY BASED DIAGNOSIS OF NEUROMUSCULAR DISEASES FOR ASSISTIVE REHABILITATION
Autor:
Sailesh Conjeti, Bijay Kumar Rout
Assistive Rehabilitation aims at developing procedures and therapies which reinstate lost body functions for individuals with disabilities. Researchers have monitored electrophysiological activity of muscles using biofeedback obtained from Electromyo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8a77c19eb4034c4ede7ce7ccfc4f9459
Autor:
Bruce Fischl, Leonie Henschel, Kersten Diers, Santiago Estrada, Martin Reuter, Sailesh Conjeti
Publikováno v:
NeuroImage, Vol 219, Iss, Pp 117012-(2020)
NeuroImage
NeuroImage 219, 117012-(2020). doi:10.1016/j.neuroimage.2020.117012
NeuroImage
NeuroImage 219, 117012-(2020). doi:10.1016/j.neuroimage.2020.117012
Traditional neuroimage analysis pipelines involve computationally intensive, time-consuming optimization steps, and thus, do not scale well to large cohort studies with thousands or tens of thousands of individuals. In this work we propose a fast and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e1cc4855588befe91eb11866b416e921
http://www.sciencedirect.com/science/article/pii/S1053811920304985
http://www.sciencedirect.com/science/article/pii/S1053811920304985
Autor:
Leonie Henschel, Sailesh Conjeti, Martin Reuter, Santiago Estrada, Kersten Diers, Bruce Fischl
Publikováno v:
Informatik aktuell ISBN: 9783658292669
Informatik aktuell
Informatik aktuell
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a87f67bbe7353684e1ff036a2ae5eaee
https://doi.org/10.1007/978-3-658-29267-6_46
https://doi.org/10.1007/978-3-658-29267-6_46