| 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 | 2067 | 2018 |
| Skin lesion analysis towards melanoma detection using deep learning network Y Li, L Shen Sensors 18 (2), 556, 2018 | 683 | 2018 |
| An objective comparison of cell-tracking algorithms V Ulman, M Maška, KEG Magnusson, O Ronneberger, C Haubold, ... Nature methods 14 (12), 1141-1152, 2017 | 604 | 2017 |
| A multi-organ nucleus segmentation challenge N Kumar, R Verma, D Anand, Y Zhou, OF Onder, E Tsougenis, H Chen, ... IEEE transactions on medical imaging 39 (5), 1380-1391, 2019 | 460 | 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 | 194 | 2019 |
| 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 | 167 | 2020 |
| Boxdiff: Text-to-image synthesis with training-free box-constrained diffusion J Xie, Y Li, Y Huang, H Liu, W Zhang, Y Zheng, MZ Shou Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 145 | 2023 |
| 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 | 144 | 2021 |
| mmformer: Multimodal medical transformer for incomplete multimodal learning of brain tumor segmentation Y Zhang, N He, J Yang, Y Li, D Wei, Y Huang, Y Zhang, Z He, Y Zheng International Conference on Medical Image Computing and Computer-Assisted …, 2022 | 123 | 2022 |
| Self-loop uncertainty: A novel pseudo-label for semi-supervised medical image segmentation Y Li, J Chen, X Xie, K Ma, Y Zheng Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 123 | 2020 |
| 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 | 110 | 2020 |
| 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 | 94 | 2021 |
| cC-GAN: A robust transfer-learning framework for HEp-2 specimen image segmentation Y Li, L Shen IEEE Access 6, 14048-14058, 2018 | 89 | 2018 |
| Revisiting Rubik’s cube: Self-supervised learning with volume-wise transformation for 3D medical image segmentation X Tao, Y Li, W Zhou, K Ma, Y Zheng Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 85 | 2020 |
| Deep learning based gastric cancer identification Y Li, X Li, X Xie, L Shen 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018 …, 2018 | 85 | 2018 |
| Deep learning based multimodal brain tumor diagnosis Y Li, L Shen Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2018 | 79 | 2018 |
| Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies P Xue, C Tang, Q Li, Y Li, Y Shen, Y Zhao, J Chen, J Wu, L Li, W Wang, ... BMC medicine 18, 1-10, 2020 | 75 | 2020 |
| Efficient and effective training of COVID-19 classification networks with self-supervised dual-track learning to rank Y Li, D Wei, J Chen, S Cao, H Zhou, Y Zhu, J Wu, L Lan, W Sun, T Qian, ... IEEE Journal of Biomedical and Health Informatics 24 (10), 2787-2797, 2020 | 74 | 2020 |
| Instance-aware self-supervised learning for nuclei segmentation X Xie, J Chen, Y Li, L Shen, K Ma, Y Zheng Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 68 | 2020 |
| Comparing methods of detecting and segmenting unruptured intracranial aneurysms on TOF-MRAS: the ADAM challenge KM Timmins, IC van der Schaaf, E Bennink, YM Ruigrok, X An, ... Neuroimage 238, 118216, 2021 | 65 | 2021 |
Yefeng ZhengProfessor, Westlake University, Hangzhou, China, IEEE Fellow, AIMBE FellowVerified email at westlake.edu.cn
