Towards defending against adversarial examples via attack-invariant features D Zhou, T Liu, B Han, N Wang, C Peng, X Gao International Conference on Machine Learning, 12835-12845, 2021 | 41 | 2021 |
Advanced zoomed diffusion-weighted imaging vs. full-field-of-view diffusion-weighted imaging in prostate cancer detection: a radiomic features study L Hu, D Zhou, C Fu, T Benkert, C Jiang, R Li, L Wei, J Zhao European Radiology 31 (3), 1760-1769, 2021 | 20 | 2021 |
Removing Adversarial Noise in Class Activation Feature Space D Zhou, N Wang, C Peng, X Gao, X Wang, J Yu, T Liu Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 17 | 2021 |
Towards multi-domain face synthesis via domain-invariant representations and multi-level feature parts D Zhou, N Wang, C Peng, Y Yu, X Yang, X Gao IEEE Transactions on Multimedia 24, 3469-3479, 2021 | 14 | 2021 |
Improving Adversarial Robustness via Mutual Information Estimation D Zhou, N Wang, X Gao, B Han, X Wang, Y Zhan, T Liu International Conference on Machine Learning, 27338-27352, 2022 | 12 | 2022 |
Adversarial training for prostate cancer classification using magnetic resonance imaging L Hu, DW Zhou, XY Guo, WH Xu, LM Wei, JG Zhao Quantitative Imaging in Medicine and Surgery 12 (6), 3276, 2022 | 12 | 2022 |
Modeling Adversarial Noise for Adversarial Training D Zhou, N Wang, B Han, T Liu International Conference on Machine Learning, 27353-27366, 2022 | 10* | 2022 |
Synthesizing High-b-Value Diffusion–weighted Imaging of the Prostate Using Generative Adversarial Networks L Hu, D Zhou, Y Zha, L Li, H He, W Xu, L Qian, Y Zhang, C Fu, H Hu, ... Radiology: Artificial Intelligence 3 (5), e200237, 2021 | 9 | 2021 |
Calculation of apparent diffusion coefficients in prostate cancer using deep learning algorithms: a pilot study L Hu, DW Zhou, CX Fu, T Benkert, YF Xiao, LM Wei, JG Zhao Frontiers in Oncology 11, 697721, 2021 | 5 | 2021 |
Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training D Zhou, N Wang, X Gao, B Han, J Yu, X Wang, T Liu arXiv preprint arXiv:2106.05453, 2021 | 5 | 2021 |
Eliminating adversarial noise via information discard and robust representation restoration D Zhou, Y Chen, N Wang, D Liu, X Gao, T Liu International Conference on Machine Learning, 42517-42530, 2023 | 2 | 2023 |
Quantization Aware Attack: Enhancing the Transferability of Adversarial Attacks across Target Models with Different Quantization Bitwidths Y Yang, C Lin, Q Li, C Shen, D Zhou, N Wang, T Liu arXiv preprint arXiv:2305.05875, 2023 | 2 | 2023 |
Strength-Adaptive Adversarial Training C Yu, D Zhou, L Shen, J Yu, B Han, M Gong, N Wang, T Liu arXiv preprint arXiv:2210.01288, 2022 | 2 | 2022 |
Phase-aware adversarial defense for improving adversarial robustness D Zhou, N Wang, H Yang, X Gao, T Liu International Conference on Machine Learning, 42724-42741, 2023 | 1 | 2023 |
Hiding Visual Information via Obfuscating Adversarial Perturbations Z Su, D Zhou, N Wang, D Liu, Z Wang, X Gao Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 1 | 2023 |
Protecting Prostate Cancer Classification from Rectal Artifacts via Targeted Adversarial Training L Hu, D Zhou, J Xu, C Lu, C Han, Z Shi, Q Zhu, X Gao, N Wang, Z Liu IEEE Journal of Biomedical and Health Informatics, 2024 | | 2024 |
Development and Validation of a Deep Learning Model to Reduce the Interference of Rectal Artifacts in MRI-based Prostate Cancer Diagnosis L Hu, X Guo, D Zhou, Z Wang, L Dai, L Li, Y Li, T Zhang, H Long, C Yu, ... Radiology: Artificial Intelligence 6 (2), e230362, 2024 | | 2024 |
Gradient constrained sharpness-aware prompt learning for vision-language models L Liu, N Wang, D Zhou, X Gao, D Liu, X Yang, T Liu arXiv preprint arXiv:2309.07866, 2023 | | 2023 |