Publisher
Springer Nature Switzerland
Reference24 articles.
1. Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629 (2018)
2. Cloak, K., et al.: Contour variation is a primary source of error when delivering post prostatectomy radiotherapy: results of the trans-tasman radiation oncology group 08.03 radiotherapy adjuvant versus early salvage (raves) benchmarking exercise. J. Med. Imag. Radiat. Oncol. 63(3), 390–398 (2019)
3. Dayani, F., et al.: Safety and outcomes of resection of butterfly glioblastoma. Neurosurg. Focus 44(6), E4 (2018)
4. Fidon, L., et al.: A dempster-shafer approach to trustworthy AI with application to fetal brain MRI segmentation. arXiv preprint arXiv:2204.02779 (2022)
5. Galdran, A., Carneiro, G., Ballester, M.A.G.: On the optimal combination of cross-entropy and soft dice losses for lesion segmentation with out-of-distribution robustness. In: Yap, M.H., Kendrick, C., Cassidy, B. (eds.) Diabetic Foot Ulcers Grand Challenge. DFUC 2022. LNCS, vol. 13797. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-26354-5_4
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献