| Simple online and realtime tracking A Bewley, Z Ge, L Ott, F Ramos, B Upcroft 2016 IEEE international conference on image processing (ICIP), 3464-3468, 2016 | 4354 | 2016 |
| Deepfruits: A fruit detection system using deep neural networks I Sa, Z Ge, F Dayoub, B Upcroft, T Perez, C McCool sensors 16 (8), 1222, 2016 | 1139 | 2016 |
| Generative OpenMax for Multi-Class Open Set Classification Z Ge, S Demyanov, Z Chen, R Garnavi The British Machine Vision Conference (BMVC 2017), 2017 | 490 | 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 33, 2020 | 378 | 2020 |
| Robust early-learning: Hindering the memorization of noisy labels X Xia, T Liu, B Han, C Gong, N Wang, Z Ge, Y Chang International Conference on Learning Representations (ICLR), 2020 | 303 | 2020 |
| 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 | 245 | 2020 |
| Semi-supervised Left Atrium Segmentation with Mutual Consistency Training Y Wu, M Xu, Z Ge, J Cai, L Zhang MICCAI 2021, 2021 | 215 | 2021 |
| 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 | 209 | 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 | 154 | 2021 |
| 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 | 151 | 2019 |
| 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 | 147 | 2017 |
| 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 | 141 | 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 | 134 | 2019 |
| 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 | 133 | 2022 |
| 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 | 126 | 2018 |
| 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 | 105 | 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 | 98 | 2022 |
| 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 | 93 | 2023 |
| FDCNet: filtering deep convolutional network for marine organism classification H Lu, Y Li, T Uemura, Z Ge, X Xu, L He, S Serikawa, H Kim Multimedia tools and applications 77, 21847-21860, 2018 | 91 | 2018 |
| 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 | 90 | 2023 |
Lie JuMonash University; University College London; Moorfields Eye HospitalVerified email at monash.edu
