| Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... arXiv preprint arXiv:1811.02629, 2018 | 2292 | 2018 |
| Med3d: Transfer learning for 3d medical image analysis S Chen, K Ma, Y Zheng arXiv preprint arXiv:1904.00625, 2019 | 589 | 2019 |
| Calibrated RGB-D salient object detection W Ji, J Li, S Yu, M Zhang, Y Piao, S Yao, Q Bi, K Ma, Y Zheng, H Lu, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 277 | 2021 |
| X2CT-GAN: reconstructing CT from biplanar X-rays with generative adversarial networks X Ying, H Guo, K Ma, J Wu, Z Weng, Y Zheng Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 276 | 2019 |
| Self-supervised feature learning for 3d medical images by playing a rubik’s cube X Zhuang, Y Li, Y Hu, K Ma, Y Yang, Y Zheng Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 196 | 2019 |
| Complex networks and deep learning for EEG signal analysis Z Gao, W Dang, X Wang, X Hong, L Hou, K Ma, M Perc Cognitive Neurodynamics 15 (3), 369-388, 2021 | 194 | 2021 |
| Deep Representation-Based Domain Adaptation for Nonstationary EEG Classification H Zhao, Q Zheng, K Ma, H Li, Y Zheng IEEE transactions on neural networks and learning systems, 2020 | 183 | 2020 |
| Learning calibrated medical image segmentation via multi-rater agreement modeling W Ji, S Yu, J Wu, K Ma, C Bian, Q Bi, J Li, H Liu, L Cheng, Y Zheng Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 177 | 2021 |
| Rubik’s cube+: A self-supervised feature learning framework for 3d medical image analysis J Zhu, Y Li, Y Hu, K Ma, SK Zhou, Y Zheng Medical image analysis 64, 101746, 2020 | 176 | 2020 |
| Mil-vt: Multiple instance learning enhanced vision transformer for fundus image classification S Yu, K Ma, Q Bi, C Bian, M Ning, N He, Y Li, H Liu, Y Zheng Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …, 2021 | 156 | 2021 |
| A channel-fused dense convolutional network for EEG-based emotion recognition Z Gao, X Wang, Y Yang, Y Li, K Ma, G Chen IEEE Transactions on Cognitive and Developmental Systems 13 (4), 945-954, 2020 | 154 | 2020 |
| Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning YQ Jiang, SE Cao, S Cao, JN Chen, GY Wang, WQ Shi, YN Deng, ... Journal of cancer research and clinical oncology 147, 821-833, 2021 | 144 | 2021 |
| Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation Y Li, J Chen, X Xie, K Ma, Y Zheng International Conference on Medical Image Computing and Computer-Assisted …, 2020 | 127 | 2020 |
| Dense cross-query-and-support attention weighted mask aggregation for few-shot segmentation X Shi, D Wei, Y Zhang, D Lu, M Ning, J Chen, K Ma, Y Zheng European Conference on Computer Vision, 151-168, 2022 | 122 | 2022 |
| Computer-aided cervical cancer diagnosis using time-lapsed colposcopic images Y Li, J Chen, P Xue, C Tang, J Chang, C Chu, K Ma, Q Li, Y Zheng, ... IEEE transactions on medical imaging 39 (11), 3403-3415, 2020 | 112 | 2020 |
| Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations HY Zhou, S Yu, C Bian, Y Hu, K Ma, Y Zheng International Conference on Medical Image Computing and Computer-Assisted …, 2020 | 111 | 2020 |
| Multimodal image registration with deep context reinforcement learning K Ma, J Wang, V Singh, B Tamersoy, YJ Chang, A Wimmer, T Chen Medical Image Computing and Computer Assisted Intervention− MICCAI 2017 …, 2017 | 108 | 2017 |
| Anomaly detection for medical images using self-supervised and translation-consistent features H Zhao, Y Li, N He, K Ma, L Fang, H Li, Y Zheng IEEE Transactions on Medical Imaging 40 (12), 3641-3651, 2021 | 99 | 2021 |
| Depthsynth: Real-time realistic synthetic data generation from cad models for 2.5 d recognition B Planche, Z Wu, K Ma, S Sun, S Kluckner, O Lehmann, T Chen, A Hutter, ... 2017 International conference on 3d vision (3DV), 1-10, 2017 | 95 | 2017 |
| Multi-task neural networks with spatial activation for retinal vessel segmentation and artery/vein classification W Ma, S Yu, K Ma, J Wang, X Ding, Y Zheng Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 91 | 2019 |
Yefeng ZhengProfessor, Westlake University, Hangzhou, China, IEEE Fellow, AIMBE Fellowยืนยันอีเมลแล้วที่ westlake.edu.cn