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 | 1770 | 2018 |
Dual-force convolutional neural networks for accurate brain tumor segmentation S Chen, C Ding, M Liu Pattern Recognition 88, 90-100, 2019 | 184 | 2019 |
Learning contextual and attentive information for brain tumor segmentation C Zhou, S Chen, C Ding, D Tao Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2019 | 136 | 2019 |
Boundary-assisted region proposal networks for nucleus segmentation S Chen, C Ding, D Tao Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 35 | 2020 |
CPP-net: Context-aware polygon proposal network for nucleus segmentation S Chen, C Ding, M Liu, J Cheng, D Tao IEEE Transactions on Image Processing 32, 980-994, 2023 | 34 | 2023 |
Brain tumor segmentation with label distribution learning and multi-level feature representation S Chen, C Ding, C Zhou Proceedings of the International MICCAI BraTS Challenge, 2017 | 6 | 2017 |