| 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 |
| The liver tumor segmentation benchmark (lits) P Bilic, P Christ, HB Li, E Vorontsov, A Ben-Cohen, G Kaissis, A Szeskin, ... Medical Image Analysis 84, 102680, 2023 | 933 | 2023 |
| The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge N Heller, F Isensee, KH Maier-Hein, X Hou, C Xie, F Li, Y Nan, G Mu, ... Medical image analysis 67, 101821, 2021 | 450 | 2021 |
| Loss odyssey in medical image segmentation J Ma, J Chen, M Ng, R Huang, Y Li, C Li, X Yang, AL Martel Medical Image Analysis 71, 102035, 2021 | 444 | 2021 |
| Segment anything in medical images J Ma, Y He, F Li, L Han, C You, B Wang Nature Communications 15 (1), 654, 2024 | 352 | 2024 |
| Multi-centre, multi-vendor and multi-disease cardiac segmentation: the M&Ms challenge VM Campello, P Gkontra, C Izquierdo, C Martin-Isla, A Sojoudi, PM Full, ... IEEE Transactions on Medical Imaging 40 (12), 3543-3554, 2021 | 274 | 2021 |
| Towards Efficient COVID-19 CT Annotation: A Benchmark for Lung and Infection Segmentation J Ma, Y Wang, X An, C Ge, Z Yu, J Chen, Q Zhu, G Dong, J He, Z He, ... Medical Physics, 2020 | 271* | 2020 |
| Abdomenct-1k: Is abdominal organ segmentation a solved problem? J Ma, Y Zhang, S Gu, C Zhu, C Ge, Y Zhang, X An, C Wang, Q Wang, ... IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (10), 6695 …, 2021 | 256 | 2021 |
| Head and neck tumor segmentation in PET/CT: the HECKTOR challenge V Oreiller, V Andrearczyk, M Jreige, S Boughdad, H Elhalawani, J Castelli, ... Medical image analysis 77, 102336, 2022 | 165 | 2022 |
| Fast and low-GPU-memory abdomen CT organ segmentation: the flare challenge J Ma, Y Zhang, S Gu, X An, Z Wang, C Ge, C Wang, F Zhang, Y Wang, ... Medical Image Analysis 82, 102616, 2022 | 102 | 2022 |
| How distance transform maps boost segmentation CNNs: an empirical study J Ma, Z Wei, Y Zhang, Y Wang, R Lv, C Zhu, C Gaoxiang, J Liu, C Peng, ... Medical Imaging with Deep Learning, 479-492, 2020 | 82 | 2020 |
| Multi-site infant brain segmentation algorithms: the iSeg-2019 challenge Y Sun, K Gao, Z Wu, G Li, X Zong, Z Lei, Y Wei, J Ma, X Yang, X Feng, ... IEEE Transactions on Medical Imaging 40 (5), 1363-1376, 2021 | 71 | 2021 |
| Unleashing the strengths of unlabeled data in pan-cancer abdominal organ quantification: the flare22 challenge J Ma, Y Zhang, S Gu, C Ge, S Ma, A Young, C Zhu, K Meng, X Yang, ... arXiv preprint arXiv:2308.05862, 2023 | 50 | 2023 |
| U-mamba: Enhancing long-range dependency for biomedical image segmentation J Ma, F Li, B Wang arXiv preprint arXiv:2401.04722, 2024 | 49 | 2024 |
| 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 | 48 | 2021 |
| Learning geodesic active contours for embedding object global information in segmentation CNNs J Ma, J He, X Yang IEEE Transactions on Medical Imaging 40 (1), 93-104, 2020 | 45 | 2020 |
| Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge A Lalande, Z Chen, T Pommier, T Decourselle, A Qayyum, M Salomon, ... Medical Image Analysis 79, 102428, 2022 | 37 | 2022 |
| The autopet challenge: towards fully automated lesion segmentation in oncologic pet/ct imaging S Gatidis, M Früh, M Fabritius, S Gu, K Nikolaou, C La Fougère, J Ye, J He, ... | 33 | 2023 |
| Automated segmentation of normal and diseased coronary arteries–the asoca challenge R Gharleghi, D Adikari, K Ellenberger, SY Ooi, C Ellis, CM Chen, R Gao, ... Computerized Medical Imaging and Graphics 97, 102049, 2022 | 33 | 2022 |
| QU-BraTS: MICCAI BraTS 2020 challenge on quantifying uncertainty in brain tumor segmentation-analysis of ranking scores and benchmarking results R Mehta, A Filos, U Baid, C Sako, R McKinley, M Rebsamen, K Dätwyler, ... The journal of machine learning for biomedical imaging 2022, 2022 | 32 | 2022 |
Jianan ChenPhD candidate, University of TorontoVerified email at mail.utoronto.ca
