| 2d-based coarse-to-fine approaches for small target segmentation in abdominal ct scans Y Zhou, Q Yu, Y Wang, L Xie, W Shen, EK Fishman, AL Yuille Deep Learning and Convolutional Neural Networks for Medical Imaging and …, 2019 | 2 | 2019 |
| 3d transunet: Advancing medical image segmentation through vision transformers J Chen, J Mei, X Li, Y Lu, Q Yu, Q Wei, X Luo, Y Xie, E Adeli, Y Wang, ... arXiv preprint arXiv:2310.07781, 2023 | 20 | 2023 |
| 3D-TransUNet for Brain Metastases Segmentation in the BraTS2023 Challenge S Yang, X Li, J Mei, J Chen, C Xie, Y Zhou arXiv preprint arXiv:2403.15735, 2024 | 2 | 2024 |
| A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021 F Falck, Y Zhou, E Rocheteau, L Shen, L Oala, G Abebe, S Roy, S Pfohl, ... arXiv preprint arXiv:2112.00179, 2021 | | 2021 |
| A fixed-point model for pancreas segmentation in abdominal CT scans Y Zhou, L Xie, W Shen, Y Wang, EK Fishman, AL Yuille International conference on medical image computing and computer-assisted …, 2017 | 381* | 2017 |
| A Flexible 2.5 D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention A Kumar, H Jiang, M Imran, C Valdes, G Leon, D Kang, P Nataraj, Y Zhou, ... arXiv preprint arXiv:2405.00130, 2024 | 1 | 2024 |
| A multiscale deep learning method for quantitative visualization of traumatic hemoperitoneum at CT: assessment of feasibility and comparison with subjective categorical estimation D Dreizin, Y Zhou, S Fu, Y Wang, G Li, K Champ, E Siegel, Z Wang, ... Radiology: Artificial Intelligence 2 (6), e190220, 2020 | 31 | 2020 |
| A Semantic Space is Worth 256 Language Descriptions: Make Stronger Segmentation Models with Descriptive Properties J Xiao, Z Zhou, W Li, S Lan, J Mei, Z Yu, A Yuille, Y Zhou, C Xie arXiv preprint arXiv:2312.13764, 2023 | 1 | 2023 |
| A Simple Data Mixing Prior for Improving Self-Supervised Learning S Ren, H Wang, Z Gao, S He, A Yuille, Y Zhou, C Xie CVPR, 2022 | 34 | 2022 |
| Abdominal multi-organ segmentation with organ-attention networks and statistical fusion Y Wang, Y Zhou, W Shen, S Park, EK Fishman, AL Yuille Medical image analysis 55, 88-102, 2019 | 209 | 2019 |
| Added value of deep learning-based liver parenchymal CT volumetry for predicting major arterial injury after blunt hepatic trauma: a decision tree analysis D Dreizin, T Chen, Y Liang, Y Zhou, F Paes, Y Wang, AL Yuille, P Roth, ... Abdominal Radiology 46, 2556-2566, 2021 | 25 | 2021 |
| Adversarial Examples for Semantic Segmentation and Object Detection C Xie, J Wang, Z Zhang, Y Zhou, L Xie, A Yuille International Conference on Computer Vision, 2017 | 1106 | 2017 |
| Adversarial metric attack and defense for person re-identification S Bai, Y Li, Y Zhou, Q Li, PHS Torr IEEE Transactions on Pattern Analysis and Machine Intelligence 43 (6), 2119-2126, 2020 | 79 | 2020 |
| Application of deep learning to pancreatic cancer detection: lessons learned from our initial experience LC Chu, S Park, S Kawamoto, Y Wang, Y Zhou, W Shen, Z Zhu, Y Xia, ... Journal of the American College of Radiology: JACR 16 (9 Pt B), 1338-1342, 2019 | 77 | 2019 |
| Bag of tricks for fgsm adversarial training Z Li, L Liu, Z Wang, Y Zhou, C Xie arXiv preprint arXiv:2209.02684, 2022 | 6 | 2022 |
| BiomedGPT: a unified and generalist biomedical generative pre-trained transformer for vision, language, and multimodal tasks K Zhang, J Yu, Z Yan, Y Liu, E Adhikarla, S Fu, X Chen, C Chen, Y Zhou, ... arXiv preprint arXiv:2305.17100, 2023 | 63 | 2023 |
| Boosting dermatoscopic lesion segmentation via diffusion models with visual and textual prompts S Du, X Wang, Y Lu, Y Zhou, S Zhang, A Yuille, K Li, Z Zhou arXiv preprint arXiv:2310.02906, 2023 | 9 | 2023 |
| CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D Networks Q Yu, Y Li, J Mei, Y Zhou, A Yuille Proceedings of the AAAI Conference on Artificial Intelligence 35 (4), 3225-3233, 2021 | 5 | 2021 |
| Can cnns be more robust than transformers? Z Wang, Y Bai, Y Zhou, C Xie arXiv preprint arXiv:2206.03452, 2022 | 52 | 2022 |
| Can temporal information help with contrastive self-supervised learning? Y Bai, H Fan, I Misra, G Venkatesh, Y Lu, Y Zhou, Q Yu, V Chandra, ... arXiv preprint arXiv:2011.13046, 2020 | 44 | 2020 |
Alan YuilleProfessor of Cognitive Science and Computer Science, Johns Hopkins University在 jhu.edu 的电子邮件经过验证
