Zongyuan (Tony) Ge

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Mingguang HeThe Hong Kong Polytechnic UniversityDirección de correo verificada de polyu.edu.hk
Zhen YuCentral Clinical School, Monash UniversityDirección de correo verificada de monash.edu
Chris McCoolProfessor, University of BonnDirección de correo verificada de uni-bonn.de
Rahil GarnaviProfessor, Director, RAIsE Hub, The Royal Melbourne Institute of Technology (RMIT)Dirección de correo verificada de rmit.edu.au
Tom DrummondMelbourne Connect Chair, School of Computing and Information Systems, University of MelbourneDirección de correo verificada de unimelb.edu.au
Alex BewleyGoogle DeepMindDirección de correo verificada de google.com
Victoria MarMonash UniversityDirección de correo verificada de monash.edu
Xuelian ChengMonash UniversityDirección de correo verificada de monash.edu
Peter CorkeQueensland University of Technology (QUT)Dirección de correo verificada de qut.edu.au
Patrick KwanMonash Institute of Medical Engineering, Monash University, Melbourne, AustraliaDirección de correo verificada de monash.edu
Conrad SandersonData61 / CSIRODirección de correo verificada de uq.edu.au
Deval MehtaFounding Member & Research Fellow at AIM for Health Lab | Monash UniversityDirección de correo verificada de monash.edu
Michael MilfordQUT Professor | Director, QUT Robotics Centre | ARC Laureate Fellow | Microsoft FellowDirección de correo verificada de qut.edu.au
Gholamreza (Reza) HaffariMonash University and Openstream AIDirección de correo verificada de monash.edu
Dwarikanath MahapatraPrincipal Scientist

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Zongyuan (Tony) Ge

Otros nombresTony Ge, Ge Zongyuan, 戈宗元

Associate Prof | Director of AIM for Health Lab | NVIDIA AI Fellowship

Dirección de correo verificada de monash.edu - Página principal

LLM Foundation Model Multi-Modal Medical AI Robotics


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Simple online and realtime tracking A Bewley, Z Ge, L Ott, F Ramos, B Upcroft IEEE International Conference on Image Processing, 2016	4749	2016
Deepfruits: A fruit detection system using deep neural networks I Sa, Z Ge, F Dayoub, B Upcroft, T Perez, C McCool MDPI Sensors 16 (8), 1222, 2016	1181	2016
Generative OpenMax for Multi-Class Open Set Classification Z Ge, S Demyanov, Z Chen, R Garnavi BMVC 2017, 2017	524	2017
Hierarchical Neural Architecture Search for Deep Stereo Matching X Cheng, Y Zhong, M Harandi, Y Dai, X Chang, H Li, T Drummond, Z Ge NeurIPS 2020, 2020	397	2020
Robust early-learning: Hindering the memorization of noisy labels X Xia, T Liu, B Han, C Gong, N Wang, Z Ge, Y Chang ICLR 2020, 2020	340	2020
Semi-supervised Left Atrium Segmentation with Mutual Consistency Training Y Wu, M Xu, Z Ge, J Cai, L Zhang MICCAI 2021, 2021	270	2021
Skin lesion segmentation via generative adversarial networks with dual discriminators B Lei, Z Xia, F Jiang, X Jiang, Z Ge, Y Xu, J Qin, S Chen, T Wang, S Wang Medical Image Analysis 64, 101716, 2020	266	2020
Mutual consistency learning for semi-supervised medical image segmentation Y Wu, Z Ge, D Zhang, M Xu, L Zhang, Y Xia, J Cai Medical Image Analysis 81, 102530, 2022	265	2022
Exploring smoothness and class-separation for semi-supervised medical image segmentation Y Wu, Z Wu, Q Wu, Z Ge, J Cai MICCAI 2022, 34-43, 2022	177	2022
Zeronas: Differentiable generative adversarial networks search for zero-shot learning C Yan, X Chang, Z Li, W Guan, Z Ge, L Zhu, Q Zheng IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021	168	2021
Skin disease recognition using deep saliency features and multimodal learning of dermoscopy and clinical images Z Ge, S Demyanov, R Chakravorty, A Bowling, R Garnavi (MICCAI 2017) International Conference on Medical Image Computing and …, 2017	164	2017
Progressive Transfer Learning and Adversarial Domain Adaptation for Cross-Domain Skin Disease Classification Z Ge, Y Gu, CP Bonnington, J Zhou IEEE Journal of Biomedical and Health Informatics, 2019	153	2019
An interpretable prediction model for identifying N7-methylguanosine sites based on XGBoost and SHAP Y Bi, D Xiang, Z Ge*, F Li, C Jia, J Song Molecular Therapy-Nucleic Acids, 2020	151	2020
A Universal Artificial Intelligence Platform for Collaborative Management of Cataracts X Wu, Z Liu, W Lai, E Long, K Zhang, J Jiang, D Lin, K Chen, T Yu, D Wu, ... British Journal of Ophthalmology, 2019	142	2019
Learning context flexible attention model for long-term visual place recognition Z Chen, L Liu, I Sa, Z Ge, M Chli IEEE Robotics and Automation Letters 3 (4), 4015-4022, 2018	136	2018
Medical visual question answering: A survey Z Lin, D Zhang, Q Tao, D Shi, G Haffari, Q Wu, M He, Z Ge Artificial Intelligence in Medicine 143, 102611, 2023	124	2023
Application of Comprehensive Artificial intelligence Retinal Expert (CARE) system: a national real-world evidence study D Lin, J Xiong, C Liu, L Zhao, Z Li, S Yu, X Wu, Z Ge, X Hu, B Wang, M Fu, ... The Lancet Digital Health 3 (8), e486-e495, 2021	118	2021
Improving Medical Images Classification with Label Noise Using Dual-Uncertainty Estimation L Ju, X Wang, L Wang, D Mahapatra, X Zhao, Q Zhou, T Liu, Z Ge IEEE Transactions on Medical Imaging, 2022	109	2022
Retinal age gap as a predictive biomarker for mortality risk Z Zhu, D Shi, P Guankai, Z Tan, X Shang, W Hu, H Liao, X Zhang, ... British Journal of Ophthalmology 107 (4), 547-554, 2023	104	2023
Hierarchically resistive skins as specific and multimetric on-throat wearable biosensors S Gong, X Zhang, XA Nguyen, Q Shi, F Lin, S Chauhan, Z Ge, W Cheng Nature Nanotechnology 18 (8), 889-897, 2023	102	2023

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