Sailesh Conjeti

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Nassir NavabProfessor of Computer Science, Technische Universität MünchenVerified email at cs.tum.edu
Abhijit Guha RoyGoogle ResearchVerified email at google.com
Amin KatouzianIBM Watson Health Imaging, Rapid DevelopmentVerified email at us.ibm.com
Debdoot SheetAssistant Professor, EE and AI, Indian Institute of Technology Kharagpur, IndiaVerified email at ee.iitkgp.ac.in
Christian WachingerMIT, Harvard medical schoolVerified email at csail.mit.edu
Jyotirmoy ChatterjeeAssociate Professor of SMST, Indian Institute of Technology, KharagpurVerified email at smst.iitkgp.ernet.in
Magdalini PaschaliPostdoctoral Scholar, Stanford UniversityVerified email at stanford.edu
Santiago EstradaDoctoral Researcher, German Center for Neurodegenerative Diseases (DZNE)Verified email at dzne.de
Prof. Dr. Martin ReuterDirector AI in Medical Imaging DZNE Bonn Germany, Radiology & Neurology at Harvard Medical SchoolVerified email at mgh.harvard.edu
Rajiv Ranjan SinghProfessor, Department of Electronics and Communication Engineering, Presidency University, BengaluruVerified email at presidencyuniversity.in
Fernando NavarroTechnische Universität MünchenVerified email at tum.de
Tanveer Syeda-MahmoodIBM Almaden Research CenterVerified email at us.ibm.com
sri phani krishna karriNIT Andhra PradeshVerified email at nitandhra.ac.in
Stephane CarlierInterventional cardiologist, CHU Ambroise Pare, MonsVerified email at umons.ac.be
Lena Maier-HeinProfessor, German Cancer Research CenterVerified email at dkfz.de
Jacinto C. NascimentoInstitute for Systems and Robotics (ISR/IST), LARSyS, Instituto Superior TécnicoVerified email at isr.ist.utl.pt
Gustavo CarneiroProfessor of AI and Machine Learning, University of SurreyVerified email at surrey.ac.uk
João Manuel R. S. TavaresFaculdade de Engenharia da Universidade do PortoVerified email at fe.up.pt
João Paulo PapaAssociate Professor, IEEE Senior, Alexander von Humboldt Research Fellow, São Paulo StateVerified email at fc.unesp.br
Zhi LuST EngineeringVerified email at stengg.com

Sailesh Conjeti

Global Product Lead, Artificial Intelligence, Olympus

Verified email at olympus.com - Homepage

Deep Learning Medical Image Computing Radiology Endoscopy Digital Pathology


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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	487	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	480	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	226*	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	208	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	198	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	145*	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	140*	2018
International conference on medical image computing and computer-assisted intervention HR Roth, L Lu, A Seff, KM Cherry, J Hoffman, S Wang	108	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	100	2019
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	99*	2017
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	84	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	82	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	71	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	61	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	56	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	48	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	45	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	43	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	40	2015
Deeply learnt hashing forests for content based image retrieval in prostate MR images A Shah, S Conjeti, N Navab, A Katouzian Medical Imaging 2016: Image Processing 9784, 302-307, 2016	38	2016

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