Ge, Zongyuan

Simple online and realtime tracking

Bewley, A., Ge, Z., Ott, L., Ramos, F., & Upcroft, B. (2016) · 2016 IEEE International Conference on Image Processing (ICIP)

Video Surveillance and Tracking MethodsTarget Tracking and Data Fusion in Sensor Networks

Cited by 4,433

Generative OpenMax for Multi-Class Open Set Classification

Ge, Z., Demyanov, S., & Garnavi, R. (2017) · Procedings of the British Machine Vision Conference 2017

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 472

Improving Medical Images Classification With Label Noise Using Dual-Uncertainty Estimation

Ju, L., Wang, X., Wang, L., Mahapatra, D., Zhao, X., Zhou, Q., Liu, T., & Ge, Z. (2022) · IEEE Transactions on Medical Imaging

Machine Learning and Data ClassificationArtificial Intelligence in Healthcare and Education

Cited by 128

A Multimodal Vision Foundation Model for Clinical Dermatology

Yan, S., Yu, Z., Primiero, C., Vico-Alonso, C., Wang, Z., Yang, L., Tschandl, P., Hu, M., Ju, L., Tan, G., et al. (2025) · Nature Medicine

Cutaneous Melanoma Detection and ManagementAI in cancer detection

Cited by 117

Vision-Language Navigation with Random Environmental Mixup

Liu, C., Zhu, F., Chang, X., Liang, X., Ge, Z., & Shen, Y.-D. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Multimodal Machine Learning ApplicationsReinforcement Learning in Robotics

Cited by 80

The Importance of Incorporating Human Factors in the Design and Implementation of Artificial Intelligence for Skin Cancer Diagnosis in the Real World

Felmingham, C. M., Adler, N. R., Ge, Z., Morton, R. L., Janda, M., & Mar, V. J. (2021) · American Journal of Clinical Dermatology

Cutaneous Melanoma Detection and ManagementArtificial Intelligence in Healthcare and Education

Cited by 57

Contrastive pre-training and linear interaction attention-based transformer for universal medical reports generation

Lin, Z., Zhang, D., Shi, D., Xu, R., Tao, Q., Wu, L., He, M., & Ge, Z. (2023) · Journal of Biomedical Informatics

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 39

CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models

Xia, P., Chen, Z., Tian, J., Gong, Y., Hou, R., Xu, Y., Wu, Z., Fan, Z., Zhou, Y., Zhu, K., et al. (2024) · Advances in Neural Information Processing Systems 37

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 32

Leading mediators of sex differences in the incidence of dementia in community-dwelling adults in the UK Biobank: a retrospective cohort study

Shang, X., Roccati, E., Zhu, Z., Kiburg, K., Wang, W., Huang, Y., Zhang, X., Zhang, X., Liu, J., Tang, S., et al. (2023) · Alzheimer's Research & Therapy

Dementia and Cognitive Impairment ResearchSex and Gender in Healthcare

Cited by 18

Derm1M: A Million-scale Vision-Language Dataset Aligned with Clinical Ontology Knowledge for Dermatology

Yan, S., Hu, M., Jiang, Y., Li, X., Fei, H., Tschandl, P., Kittler, H., & Ge, Z. (2025) · arXiv

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 13

Using artificial intelligence to semi-automate trustworthiness assessment of randomized controlled trials: a case study

Au, L. S., Qu, L., Nielsen, J., Ge, Z., Gurrin, L. C., Mol, B. W., & Wang, R. (2025) · Journal of Clinical Epidemiology

Academic integrity and plagiarismMeta-analysis and systematic reviews

Cited by 9

Revamping AI models in dermatology: overcoming critical challenges for enhanced skin lesion diagnosis

Mehta, D., Betz-Stablein, B., Nguyen, T. D., Gal, Y., Bowling, A., Haskett, M., Sashindranath, M., Bonnington, P., Mar, V., Soyer, H. P., et al. (2023) · arXiv

Cutaneous Melanoma Detection and ManagementAI in cancer detection

Cited by 3