S
Sailesh Conjeti
Researcher at German Center for Neurodegenerative Diseases
Publications - 78
Citations - 2606
Sailesh Conjeti is an academic researcher from German Center for Neurodegenerative Diseases. The author has contributed to research in topics: Segmentation & Convolutional neural network. The author has an hindex of 21, co-authored 78 publications receiving 1712 citations. Previous affiliations of Sailesh Conjeti include Technische Universität München & Indian Institute of Technology Kharagpur.
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Journal ArticleDOI
ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks.
Abhijit Guha Roy,Sailesh Conjeti,Sri Phani Krishna Karri,Debdoot Sheet,Amin Katouzian,Christian Wachinger,Nassir Navab +6 more
TL;DR: A new fully convolutional deep architecture, termed ReLayNet, is proposed for end-to-end segmentation of retinal layers and fluid masses in eye OCT scans, validated on a publicly available benchmark dataset with comparisons against five state-of-the-art segmentation methods.
Journal ArticleDOI
FastSurfer - A fast and accurate deep learning based neuroimaging pipeline
Leonie Henschel,Sailesh Conjeti,Santiago Estrada,Kersten Diers,Bruce Fischl,Martin Reuter,Martin Reuter +6 more
TL;DR: This work proposes a fast and accurate deep learning based neuroimaging pipeline for the automated processing of structural human brain MRI scans, replicating FreeSurfer’s anatomical segmentation including surface reconstruction and cortical parcellation.
Journal ArticleDOI
QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy.
Abhijit Guha Roy,Sailesh Conjeti,Sailesh Conjeti,Nassir Navab,Nassir Navab,Christian Wachinger,Alzheimer’s Disease Neuroimaging Initiative +6 more
TL;DR: QuickNAT, a fully convolutional, densely connected neural network that segments a MRI brain scan in 20 s, is introduced and achieves superior segmentation accuracy and reliability in comparison to state‐of‐the‐art methods, while being orders of magnitude faster.
Journal ArticleDOI
A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals
TL;DR: This work proposes a neural network driven based solution to learning driving-induced stress patterns and correlating it with statistical, structural and time-frequency changes observed in the recorded biosignals, concluded that Layer Recurrent Neural Networks are most optimal for stress level detection.
Journal ArticleDOI
Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data
Justin Guinney,Tao Wang,Teemu D. Laajala,Teemu D. Laajala,Kimberly Kanigel Winner,J. Christopher Bare,Elias Chaibub Neto,Suleiman A. Khan,Gopal Peddinti,Antti Airola,Tapio Pahikkala,Tuomas Mirtti,Thomas Yu,Brian M. Bot,Liji Shen,Kald Abdallah,Thea Norman,Stephen H. Friend,Gustavo Stolovitzky,Howard R. Soule,Christopher Sweeney,Charles J. Ryan,Howard I. Scher,Oliver Sartor,Yang Xie,Tero Aittokallio,Tero Aittokallio,Fang Liz Zhou,James C. Costello,Catalina Anghe,Helia Azima,Robert Baertsch,Pedro J Ballester,Chris Bare,Vinayak Bhandari,Cuong C Dang,Maria Bekker-Nielsen Dunbar,Ann-Sophie Buchardt,Ljubomir Buturovic,Da Cao,Prabhakar Chalise,Junwoo Cho,Tzu-Ming Chu,R Yates Coley,Sailesh Conjeti,Sara Correia,Ziwei Dai,Junqiang Dai,Philip Dargatz,Sam Delavarkhan,Detian Deng,Ankur Dhanik,Yu Du,Aparna Elangovan,Shellie D. Ellis,Laura L. Elo,Shadrielle Melijah G. Espiritu,Fan Fan,Ashkan B Farshi,Ana Freitas,Brooke L. Fridley,Christiane Fuchs,Eyal Gofer,Gopalacharyulu Peddinti,Stefan Graw,Russell Greiner,Yuanfang Guan,Jing Guo,Pankaj Gupta,Anna I Guyer,Jiawei Han,Niels Richard Hansen,Billy Hw Chang,Outi Hirvonen,Barbara Huang,Chao Huang,Jinseub Hwang,Joseph G. Ibrahim,Vivek Jayaswa,Jouhyun Jeon,Zhicheng Ji,Deekshith Juvvadi,Sirkku Jyrkkiö,Kimberly Kanigel-Winner,Amin Katouzian,Marat D. Kazanov,Shahin Khayyer,Dalho Kim,Agnieszka Kitlas Golińska,Devin C. Koestler,Fernanda Kokowicz,Ivan Kondofersky,Norbert Krautenbacher,Damjan Krstajic,Luke Kumar,Christoph Kurz,Matthew Kyan,Michael Laimighofer,Eunjee Lee,Wojciech Lesinski,Miaozhu Li,Ye Li,Qiuyu Lian,Xiaotao Liang,Minseong Lim,Henry Lin,Xihui Lin,Jing Lu,Mehrad Mahmoudian,Roozbeh Manshaei,Richard Meier,Dejan Miljkovic,Krzysztof Mnich,Nassir Navab,Elias Chaibub Neto,Yulia Newton,Subhabrata Pal,Byeongju Park,Jaykumar Patel,Swetabh Pathak,Alejandrina Pattin,Donna P. Ankerst,Jian Peng,Anne H Petersen,Robin Philip,Stephen R. Piccolo,Sebastian Pölsterl,Aneta Polewko-Klim,Karthik Rao,Xiang Ren,Miguel Rocha,Witold R. Rudnicki,Witold R. Rudnicki,Hyunnam Ryu,Hagen Scherb,Raghav Sehgal,Fatemeh Seyednasrollah,Jingbo Shang,Bin Shao,Howard Sher,Motoki Shiga,Artem Sokolov,Julia F. Söllner,Lei Song,Josh Stuart,Ren Sun,Nazanin Tahmasebi,Kar-Tong Tan,Lisbeth Tomaziu,Joseph Usset,Yeeleng S Vang,Roberto Vega,Vítor Vieira,David Wang,Difei Wang,Junmei Wang,Lichao Wang,Sheng Wang,Yue Wang,Russell D. Wolfinger,Christopher K. Wong,Zhenke Wu,Jinfeng Xiao,Xiaohui Xie,Doris Xin,Hojin Yang,Nancy Yu,Xiang Yu,Sulmaz Zahedi,Massimiliano Zanin,Chihao Zhang,Jingwen Zhang,Shihua Zhang,Yanchun Zhang,Hongtu Zhu,Shanfeng Zhu,Yuxin Zhu +176 more
TL;DR: 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge, and the top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function.