Zhang, Zizhao

FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

Sohn, K., Berthelot, D., Li, C.-L., Zhang, Z., Carlini, N., Cubuk, E. D., Kurakin, A., Zhang, H., & Raffel, C. (2020) · arXiv

Machine Learning and AlgorithmsDomain Adaptation and Few-Shot Learning

Cited by 2,892

MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network

Zhang, Z., Xie, Y., Xing, F., McGough, M., & Yang, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsAI in cancer detection

Cited by 354

Pathologist-level interpretable whole-slide cancer diagnosis with deep learning

Zhang, Z., Chen, P., McGough, M., Xing, F., Wang, C., Bui, M., Xie, Y., Sapkota, M., Cui, L., Dhillon, J., et al. (2019) · Nature Machine Intelligence

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 311

Reducing Uncertainty in Undersampled MRI Reconstruction With Active Acquisition

Zhang, Z., Romero, A., Muckley, M. J., Vincent, P., Yang, L., & Drozdzal, M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced MRI Techniques and ApplicationsImage and Signal Denoising Methods

Cited by 106

Breast mass classification via deeply integrating the contextual information from multi-view data

Wang, H., Feng, J., Zhang, Z., Su, H., Cui, L., He, H., & Liu, L. (2018) · Pattern Recognition

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 68

OpenResearcher: Unleashing AI for Accelerated Scientific Research

Zheng, Y., Sun, S., Qiu, L., Ru, D., Jiayang, C., Li, X., Lin, J., Wang, B., Luo, Y., Pan, R., et al. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Topic ModelingArtificial Intelligence Applications

Cited by 5