Sailesh Conjeti
Sailesh Conjeti
Global Product Lead, Artificial Intelligence, Olympus
Verified email at - Homepage
Cited by
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Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS …
D Stoyanov, Z Taylor, G Carneiro, T Syeda-Mahmood, A Martel, ...
Springer, 2018
ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks
AG Roy, S Conjeti, SPK Karri, D Sheet, A Katouzian, C Wachinger, ...
Biomedical optics express 8 (8), 3627-3642, 2017
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
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
A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals
RR Singh, S Conjeti, R Banerjee
Biomedical Signal Processing and Control 8 (6), 740-754, 2013
Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open …
PCCD Community
The Lancet Oncology, 2016
Generalizability vs. Robustness: Investigating Medical Imaging Networks Using Adversarial Examples
M Paschali, S Conjeti, F Navarro, N Navab
Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018
International conference on medical image computing and computer-assisted intervention
HR Roth, L Lu, A Seff, KM Cherry, J Hoffman, S Wang
Error corrective boosting for learning fully convolutional networks with limited data
AG Roy, S Conjeti, D Sheet, A Katouzian, N Navab, C Wachinger
Medical Image Computing and Computer Assisted Intervention− MICCAI 2017 …, 2017
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
Inherent brain segmentation quality control from fully convnet monte carlo sampling
AG Roy, S Conjeti, N Navab, C Wachinger
Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018
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
Multiple instance learning of deep convolutional neural networks for breast histopathology whole slide classification
K Das, S Conjeti, AG Roy, J Chatterjee, D Sheet
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018 …, 2018
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
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
Human motion analysis with deep metric learning
H Coskun, DJ Tan, S Conjeti, N Navab, F Tombari
Proceedings of the European Conference on Computer Vision (ECCV), 667-683, 2018
An approach for real-time stress-trend detection using physiological signals in wearable computing systems for automotive drivers
RR Singh, S Conjeti, R Banerjee
2011 14th International IEEE Conference on Intelligent Transportation …, 2011
Complex fully convolutional neural networks for MR image reconstruction
MA Dedmari, S Conjeti, S Estrada, P Ehses, T Stöcker, M Reuter
Machine Learning for Medical Image Reconstruction: First International …, 2018
Lumen Segmentation in Intravascular Optical Coherence Tomography using Backscattering Tracked and Initialized Random Walks
A Guha Roy, S Conjeti, S Carlier, P Dutta, A Kastrati, A Laine, N Navab, ...
IEEE Journal of Biomedical and Health Informatics, 2015
Assessment of driver stress from physiological signals collected under real-time semi-urban driving scenarios
RR Singh, S Conjeti, R Banerjee
International Journal of Computational Intelligence Systems 7 (5), 909-923, 2014
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