Hongwei Bran Li

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Coauteurs

Bjoern MenzeUniversität ZürichAdresse e-mail validée de uzh.ch
Bene WiestlerAI for Image-Guided Diagnosis and Therapy, TUMAdresse e-mail validée de tum.de
Suprosanna ShitUniversity of Zurich | ETH AI CenterAdresse e-mail validée de uzh.ch
Jianguo ZhangProfessor, Southern University of Science and TechnologyAdresse e-mail validée de sustech.edu.cn
Johannes C. PaetzoldImperial College LondonAdresse e-mail validée de ic.ac.uk
Anjany SekuboyinaPhD Student, Technical University of MunichAdresse e-mail validée de tum.de
Yu ZhaoTencent; Technical University of Munich (TUM)Adresse e-mail validée de tencent.com
Ivan EzhovTechnical University of MunichAdresse e-mail validée de tum.de
Florian KoflerUniversity of ZurichAdresse e-mail validée de tum.de
Jan KirschkeNeuroradiologe, Technische Universität MünchenAdresse e-mail validée de tum.de
Daniel RueckertTechnical University of Munich and Imperial College LondonAdresse e-mail validée de tum.de
Wei-Shi ZhengProfessor @ SUN YAT-SEN UNIVERSITYAdresse e-mail validée de ieee.org
Marie PiraudHelmholtz AI, Helmoltz Zentrum MünchenAdresse e-mail validée de helmholtz-muenchen.de
Xiaobin HuTencent Youtu Lab;Technische Universität München (TUM)Adresse e-mail validée de tum.de
Kuangyu ShiUniversity of Bern/Technical University of MunichAdresse e-mail validée de unibe.ch
Giles TettehDoctoral Candidate, Technische Universität München (TUM)Adresse e-mail validée de tum.de
Jana LipkovaHarvard Medical School, Brigham and Women's HospitalAdresse e-mail validée de bwh.harvard.edu
Jiazhen PanPhD Candidate at Technical University of MunichAdresse e-mail validée de tum.de
Ruixuan WangSun Yat-Sen UniversityAdresse e-mail validée de mail.sysu.edu.cn
Dr. Oliver SchoppeTechnical University of MunichAdresse e-mail validée de Oliver-Schoppe.de

Suivre

Hongwei Bran Li

Autres nomsHongwei Li, 李宏伟

Martinos Center, MGH/Harvard Medical School

Adresse e-mail validée de mgh.harvard.edu - Page d'accueil

Medical Image Analysis ML


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... Preprint, 2018	2166	2018
The liver tumor segmentation benchmark (LiTS) P Bilic, P Christ, HB Li*, E Vorontsov, A Ben-Cohen, G Kaissis, ... Medical Image Analysis, 2022	1374	2022
Multi-centre, multi-vendor and multi-disease cardiac segmentation: the M&Ms challenge VM Campello, P Gkontra, C Izquierdo, C Martin-Isla, A Sojoudi, PM Full, ... IEEE Transactions on Medical Imaging, 2021	396	2021
Standardized assessment of automatic segmentation of white matter hyperintensities and results of the WMH segmentation challenge HJ Kuijf, JM Biesbroek, J De Bresser, R Heinen, S Andermatt, M Bento, ... IEEE Transactions on Medical Imaging, 2019	330	2019
VerSe: a vertebrae labelling and segmentation benchmark for multi-detector CT images A Sekuboyina, ME Husseini, A Bayat, M Löffler, H Liebl, H Li, G Tetteh, ... Medical Image Analysis, 2021	288	2021
Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images H Li, G Jiang, J Zhang, R Wang, Z Wang, WS Zheng, B Menze NeuroImage, 2018	260	2018
Federated learning enables big data for rare cancer boundary detection S Pati, U Baid, B Edwards, M Sheller, SH Wang, GA Reina, P Foley, ... Nature Communications, 2022	234	2022
Knowledge-aided convolutional neural network for small organ segmentation Y Zhao, H Li, S Wan, A Sekuboyina, X Hu, G Tetteh, M Piraud, B Menze IEEE journal of biomedical and health informatics, 2019	205	2019
Automated whole-body bone lesion detection for multiple myeloma on 68Ga-pentixafor PET/CT imaging using deep learning methods L Xu, G Tetteh, J Lipkova, Y Zhao, H Li, P Christ, M Piraud, A Buck, K Shi, ... Contrast media & molecular imaging, 2018	157	2018
DiamondGAN: unified multi-modal generative adversarial networks for MRI sequences synthesis H Li, JC Paetzold, A Sekuboyina, F Kofler, J Zhang, JS Kirschke, ... MICCAI'2019, 2019	97	2019
Deep learning-enabled multi-organ segmentation in whole-body mouse scans O Schoppe, C Pan, J Coronel, H Mai, Z Rong, MI Todorov, A Müskes, ... Nature Communications, 2020	91	2020
Cardiac segmentation on late gadolinium enhancement MRI: a benchmark study from multi-sequence cardiac MR segmentation challenge X Zhuang, J Xu, X Luo, C Chen, C Ouyang, D Rueckert, VM Campello, ... Medical Image Analysis, 2022	78	2022
Comparing methods of detecting and segmenting unruptured intracranial aneurysms on TOF-MRAS: the ADAM challenge KM Timmins, IC van der Schaaf, E Bennink, YM Ruigrok, X An, ... Neuroimage 238, 118216, 2021	70	2021
The brain tumor segmentation (BraTS) challenge 2023: focus on pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs) AF Kazerooni, N Khalili, X Liu, D Haldar, Z Jiang, SM Anwar, J Albrecht, ... ArXiv, arXiv: 2305.17033 v7, 2024	68	2024
Relationformer:a unified framework for image-to-graph generation S Shit, R Koner, B Wittmann, J Paetzold, I Ezhov, H Li, J Pan, ... ECCV'2022, 2022	62	2022
The brain tumor segmentation (brats) challenge 2023: glioma segmentation in sub-saharan Africa patient population (brats-africa) M Adewole, JD Rudie, A Gbdamosi, O Toyobo, C Raymond, D Zhang, ... ArXiv, 2023	58	2023
Red-GAN: attacking class imbalance via conditioned generation. Yet another medical imaging perspective AB Qasim, I Ezhov, S Shit, O Schoppe, JC Paetzold, A Sekuboyina, ... MIDL'2020, 2020	57	2020
Deep-learning generated synthetic double inversion recovery images improve multiple sclerosis lesion detection T Finck, H Li, L Grundl, P Eichinger, M Bussas, M Mühlau, B Menze, ... Investigative Radiology, 2020	54	2020
Coarse-to-fine adversarial networks and zone-based uncertainty analysis for NK/T-cell lymphoma segmentation in CT/PET images X Hu, R Guo, J Chen, H Li, D Waldmannstetter, Y Zhao, B Li, K Shi, ... IEEE journal of biomedical and health informatics, 2020	48	2020
Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient F Kofler, I Ezhov, F Isensee, F Balsiger, C Berger, M Koerner, B Demiray, ... MELBA, 2021	47	2021

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