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Ferrante, Matteo; Rinaldi, Lisa; Botta, Francesca; Hu, Xiaobin; Dolp, Andreas; Minotti, Marta; De Piano, Francesca; Funicelli, Gianluigi; Volpe, Stefania; Bellerba, Federica; De Marco, Paolo; Raimondi, Sara; Rizzo, Stefania; Shi, Kuangyu; Cremonesi, Marta; Jereczek-Fossa, Barbara A; Spaggiari, Lorenzo; De Marinis, Filippo; Orecchia, Roberto and Origgi, Daniela (2022). Application of nnU-Net for Automatic Segmentation of Lung Lesions on CT Images and Its Implication for Radiomic Models. Journal of clinical medicine, 11(24) MDPI 10.3390/jcm11247334
Guo, Rui; Hu, Xiaobin; Song, Haoming; Xu, Pengpeng; Xu, Haoping; Rominger, Axel; Lin, Xiaozhu; Menze, Bjoern; Li, Biao; Shi, Kuangyu (2021). Weakly supervised deep learning for determining the prognostic value of 18F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type. European journal of nuclear medicine and molecular imaging, 48(10), pp. 3151-3161. Springer 10.1007/s00259-021-05232-3
Hu, Xiaobin; Guo, Rui; Chen, Jieneng; Li, Hongwei; Waldmannstetter, Diana; Zhao, Yu; Li, Biao; Shi, Kuangyu; Menze, Bjoern (2020). Coarse-to-Fine Adversarial Networks and Zone-Based Uncertainty Analysis for NK/T-Cell Lymphoma Segmentation in CT/PET Images. IEEE journal of biomedical and health informatics, 24(9), pp. 2599-2608. Institute of Electrical and Electronics Engineers 10.1109/JBHI.2020.2972694
