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Hongwei Bran Li
Hongwei Bran Li
Sonstige NamenHongwei Li, 李 宏伟
Martinos Center, MGH/Harvard Medical School
Bestätigte E-Mail-Adresse bei mgh.harvard.edu - Startseite
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Zitiert von
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Jahr
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
19912018
The liver tumor segmentation benchmark (LiTS)
P Bilic*, P Christ*, HB Li*, E Vorontsov, A Ben-Cohen, G Kaissis, ...
Medical Image Analysis, 2022
11922022
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
3472021
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
2942019
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
2472018
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
2382021
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
2002019
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
1872022
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
1542018
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
932019
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
812020
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
702022
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
592021
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
532022
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
532020
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
462020
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
452020
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 preprint arXiv:2305.17033, 2023
442023
Cross-view relation networks for mammogram mass detection
J Ma, S Liang, X Li, H Li, BH Menze, R Zhang, WS Zheng
ICPR'2020, 2019
422019
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, ...
arXiv preprint arXiv:2103.06205, 2021
412021
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