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For: Kawata Y, Arimura H, Ikushima K, Jin Z, Morita K, Tokunaga C, Yabu-Uchi H, Shioyama Y, Sasaki T, Honda H, Sasaki M. Impact of pixel-based machine-learning techniques on automated frameworks for delineation of gross tumor volume regions for stereotactic body radiation therapy. Phys Med. 2017;42:141-149. [PMID: 29173908 DOI: 10.1016/j.ejmp.2017.08.012] [Cited by in Crossref: 16] [Cited by in F6Publishing: 18] [Article Influence: 3.2] [Reference Citation Analysis]
Number Citing Articles
1 Sharma A, Tselykh A. Machine Learning-Enabled Estimation System Using Fuzzy Cognitive Mapping: A Review. Proceedings of Third International Conference on Computing, Communications, and Cyber-Security 2023. [DOI: 10.1007/978-981-19-1142-2_39] [Reference Citation Analysis]
2 Mancosu P, Lambri N, Castiglioni I, Dei D, Iori M, Loiacono D, Russo S, Talamonti C, Villaggi E, Scorsetti M, Avanzo M. Applications of artificial intelligence in stereotactic body radiation therapy. Phys Med Biol 2022;67:16TR01. [DOI: 10.1088/1361-6560/ac7e18] [Reference Citation Analysis]
3 Kao Y, Yang J. Deep learning-based auto-segmentation of lung tumor PET/CT scans: a systematic review. Clin Transl Imaging. [DOI: 10.1007/s40336-022-00482-z] [Reference Citation Analysis]
4 Fiz F, Iori M, Fioroni F, Biroli M, D’agostino GR, Gelardi F, Erba PA, Versari A, Chiti A, Sollini M. Molecular Guidance for Planning External Beam Radiation Therapy in Oncology. Nuclear Oncology 2022. [DOI: 10.1007/978-3-319-26067-9_91-2] [Reference Citation Analysis]
5 Fiz F, Iori M, Fioroni F, Biroli M, D’agostino GR, Gelardi F, Erba PA, Versari A, Chiti A, Sollini M. Molecular Guidance for Planning External Beam Radiation Therapy in Oncology. Nuclear Oncology 2022. [DOI: 10.1007/978-3-319-26067-9_91-1] [Reference Citation Analysis]
6 Ebrahimi S, Lim GJ. A reinforcement learning approach for finding optimal policy of adaptive radiation therapy considering uncertain tumor biological response. Artif Intell Med 2021;121:102193. [PMID: 34763808 DOI: 10.1016/j.artmed.2021.102193] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
7 Sadaghiani MS, Rowe SP, Sheikhbahaei S. Applications of artificial intelligence in oncologic 18F-FDG PET/CT imaging: a systematic review. Ann Transl Med 2021;9:823. [PMID: 34268436 DOI: 10.21037/atm-20-6162] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 5.0] [Reference Citation Analysis]
8 Yin L, Cao Z, Wang K, Tian J, Yang X, Zhang J. A review of the application of machine learning in molecular imaging. Ann Transl Med 2021;9:825. [PMID: 34268438 DOI: 10.21037/atm-20-5877] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
9 Yakar M, Etiz D. Artificial intelligence in radiation oncology. Artif Intell Med Imaging 2021; 2(2): 13-31 [DOI: 10.35711/aimi.v2.i2.13] [Cited by in CrossRef: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Cui Y, Arimura H, Nakano R, Yoshitake T, Shioyama Y, Yabuuchi H. Automated approach for segmenting gross tumor volumes for lung cancer stereotactic body radiation therapy using CT-based dense V-networks. J Radiat Res. 2021;62:346-355. [PMID: 33480438 DOI: 10.1093/jrr/rraa132] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
11 Davey A, van Herk M, Faivre-Finn C, Brown S, McWilliam A. Automated gross tumor volume contour generation for large-scale analysis of early-stage lung cancer patients planned with 4D-CT. Med Phys 2021;48:724-32. [PMID: 33290579 DOI: 10.1002/mp.14644] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
12 Nakano R, Arimura H, Haekal M, Ohga S. Automated segmentation framework of lung gross tumor volumes on 3D planning CT images using dense V-Net deep learning. International Forum on Medical Imaging in Asia 2019 2019. [DOI: 10.1117/12.2521509] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
13 Cagni E, Botti A, Wang Y, Iori M, Petit SF, Heijmen BJ. Pareto-optimal plans as ground truth for validation of a commercial system for knowledge-based DVH-prediction. Physica Medica 2018;55:98-106. [DOI: 10.1016/j.ejmp.2018.11.002] [Cited by in Crossref: 16] [Cited by in F6Publishing: 16] [Article Influence: 4.0] [Reference Citation Analysis]
14 Močnik D, Ibragimov B, Xing L, Strojan P, Likar B, Pernuš F, Vrtovec T. Segmentation of parotid glands from registered CT and MR images. Phys Med 2018;52:33-41. [PMID: 30139607 DOI: 10.1016/j.ejmp.2018.06.012] [Cited by in Crossref: 23] [Cited by in F6Publishing: 25] [Article Influence: 5.8] [Reference Citation Analysis]
15 Ouabida E, Essadike A, Bouzid A. Automated segmentation of ophthalmological images by an optical based approach for early detection of eye tumor growing. Phys Med 2018;48:37-46. [PMID: 29728227 DOI: 10.1016/j.ejmp.2018.03.014] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 1.5] [Reference Citation Analysis]
16 Gong J, Liu JY, Wang LJ, Sun XW, Zheng B, Nie SD. Automatic detection of pulmonary nodules in CT images by incorporating 3D tensor filtering with local image feature analysis. Phys Med 2018;46:124-33. [PMID: 29519398 DOI: 10.1016/j.ejmp.2018.01.019] [Cited by in Crossref: 48] [Cited by in F6Publishing: 49] [Article Influence: 12.0] [Reference Citation Analysis]