Kamu erişimi zorunlu olan makaleler - Sailesh ConjetiDaha fazla bilgi edinin
Hiçbir yerde sunulmuyor: 3
Survival analysis for high-dimensional, heterogeneous medical data: Exploring feature extraction as an alternative to feature selection
S Pölsterl, S Conjeti, N Navab, A Katouzian
Artificial intelligence in medicine 72, 1-11, 2016
Zorunlu olanlar: US National Institutes of Health
Computer‐aided molecular pathology interpretation in exploring prospective markers for oral submucous fibrosis progression
A Anura, S Conjeti, RK Das, M Pal, RR Paul, S Bag, AK Ray, J Chatterjee
Head & neck 38 (5), 653-669, 2016
Zorunlu olanlar: Council of Scientific and Industrial Research, India
Creating a standardized tool for the evaluation and comparison of artificial intelligence–based computer-aided detection programs in colonoscopy: a modified Delphi approach
SRV Gadi, Y Mori, M Misawa, JE East, C Hassan, A Repici, MF Byrne, ...
Gastrointestinal Endoscopy, 2024
Zorunlu olanlar: National Institute for Health Research, UK
Bir yerde sunuluyor: 8
Fastsurfer-a fast and accurate deep learning based neuroimaging pipeline
L Henschel, S Conjeti, S Estrada, K Diers, B Fischl, M Reuter
NeuroImage 219, 117012, 2020
Zorunlu olanlar: US Department of Defense, US National Institutes of Health, Canadian …
QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy
AG Roy, S Conjeti, N Navab, C Wachinger, ...
NeuroImage 186, 713-727, 2019
Zorunlu olanlar: US Department of Defense, US National Institutes of Health, Canadian …
Bayesian QuickNAT: Model uncertainty in deep whole-brain segmentation for structure-wise quality control
AG Roy, S Conjeti, N Navab, C Wachinger, ...
NeuroImage 195, 11-22, 2019
Zorunlu olanlar: US Department of Defense, US National Institutes of Health, Canadian …
CATARACTS: Challenge on automatic tool annotation for cataRACT surgery
H Al Hajj, M Lamard, PH Conze, S Roychowdhury, X Hu, G Maršalkaitė, ...
Medical image analysis 52, 24-41, 2019
Zorunlu olanlar: Fundação para a Ciência e a Tecnologia, Portugal, Federal Ministry of …
FatSegNet: A fully automated deep learning pipeline for adipose tissue segmentation on abdominal dixon MRI
S Estrada, R Lu, S Conjeti, X Orozco‐Ruiz, J Panos‐Willuhn, ...
Magnetic resonance in medicine 83 (4), 1471-1483, 2020
Zorunlu olanlar: US National Institutes of Health, Federal Ministry of Education and Research …
A DREAM challenge to build prediction models for short-term discontinuation of docetaxel in metastatic castration-resistant prostate cancer
F Seyednasrollah, DC Koestler, T Wang, SR Piccolo, R Vega, R Greiner, ...
JCO clinical cancer informatics 1, 1-15, 2017
Zorunlu olanlar: US National Institutes of Health, Helmholtz Association, Academy of Finland …
A for-loop is all you need. For solving the inverse problem in the case of personalized tumor growth modeling
I Ezhov, M Rosier, L Zimmer, F Kofler, S Shit, JC Paetzold, K Scibilia, ...
Machine Learning for Health, 566-577, 2022
Zorunlu olanlar: German Research Foundation, European Commission
through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article …
A Anura, S Conjeti, S Bag
Zorunlu olanlar: Council of Scientific and Industrial Research, India
Yayıncılık ve maddi kaynak bilgileri otomatik olarak bir bilgisayar programı tarafından belirlenmektedir