Yang, Xiaokang

A deep learning system for detecting diabetic retinopathy across the disease spectrum

Dai, L., Wu, L., Li, H., Cai, C., Wu, Q., Kong, H., Liu, R., Wang, X., Hou, X., Liu, Y., et al. (2021) · Nature Communications

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 620

End-to-End Reconstruction-Classification Learning for Face Forgery Detection

Cao, J., Ma, C., Yao, T., Chen, S., Ding, S., & Yang, X. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Digital Media Forensic DetectionGenerative Adversarial Networks and Image Synthesis

Cited by 428

FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead

Chen, K., Han, T., Gong, J., Bai, L., Ling, F., Luo, J.-J., Chen, X., Ma, L., Zhang, T., Su, R., et al. (2023) · arXiv

Meteorological Phenomena and SimulationsHydrological Forecasting Using AI

Cited by 396

A deep learning system for predicting time to progression of diabetic retinopathy

Dai, L., Sheng, B., Chen, T., Wu, Q., Liu, R., Cai, C., Wu, L., Yang, D., Hamzah, H., Liu, Y., et al. (2024) · Nature Medicine

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 339

Unsupervised deep learning for optical flow estimation

Ren, Z., Yan, J., Ni, B., Liu, B., Yang, X., & Zha, H. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence

Advanced Vision and ImagingVideo Surveillance and Tracking Methods

Cited by 296

Artificial intelligence in diabetes management: Advancements, opportunities, and challenges

Guan, Z., Li, H., Liu, R., Cai, C., Liu, Y., Li, J., Wang, X., Huang, S., Wu, L., Liu, D., et al. (2023) · Cell Reports Medicine

Artificial Intelligence in Healthcare and EducationDiabetes Management and Research

Cited by 287

Integrated image-based deep learning and language models for primary diabetes care

Li, J., Guan, Z., Wang, J., Cheung, C. Y., Zheng, Y., Lim, L.-L., Lim, C. C., Ruamviboonsuk, P., Raman, R., Corsino, L., et al. (2024) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 250

Exploring Frequency Adversarial Attacks for Face Forgery Detection

Jia, S., Ma, C., Yao, T., Yin, B., Ding, S., & Yang, X. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Digital Media Forensic DetectionAdversarial Robustness in Machine Learning

Cited by 97

Progressively Parsing Interactional Objects for Fine Grained Action Detection

Ni, B., Yang, X., & Gao, S. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Human Pose and Action RecognitionHand Gesture Recognition Systems

Cited by 69

Learning Multi-View Interactional Skeleton Graph for Action Recognition

Wang, M., Ni, B., & Yang, X. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Human Pose and Action RecognitionHand Gesture Recognition Systems

Cited by 67

MedSeg-R: Reasoning Segmentation in Medical Images with Multimodal Large Language Models

Huang, Y., Peng, Z., Zhao, Y., Yang, P., Yang, X., & Shen, W. (2025) · arXiv

Multimodal Machine Learning ApplicationsMedical Image Segmentation Techniques

Cited by 15

High-resolution real-space reconstruction of cryo-EM structures using a neural field network

Huang, Y., Zhu, C., Yang, X., & Liu, M. (2024) · Nature Machine Intelligence

Advanced Electron Microscopy Techniques and ApplicationsProtein Structure and Dynamics

Cited by 9