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For: Bianconi F, Fravolini ML, Pizzoli S, Palumbo I, Minestrini M, Rondini M, Nuvoli S, Spanu A, Palumbo B. Comparative evaluation of conventional and deep learning methods for semi-automated segmentation of pulmonary nodules on CT. Quant Imaging Med Surg 2021;11:3286-305. [PMID: 34249654 DOI: 10.21037/qims-20-1356] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
1 Katase S, Ichinose A, Hayashi M, Watanabe M, Chin K, Takeshita Y, Shiga H, Tateishi H, Onozawa S, Shirakawa Y, Yamashita K, Shudo J, Nakamura K, Nakanishi A, Kuroki K, Yokoyama K. Development and performance evaluation of a deep learning lung nodule detection system. BMC Med Imaging 2022;22:203. [DOI: 10.1186/s12880-022-00938-8] [Reference Citation Analysis]
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5 Bianconi F, Fravolini ML, Palumbo I, Pascoletti G, Nuvoli S, Rondini M, Spanu A, Palumbo B. Impact of Lesion Delineation and Intensity Quantisation on the Stability of Texture Features from Lung Nodules on CT: A Reproducible Study. Diagnostics (Basel) 2021;11:1224. [PMID: 34359305 DOI: 10.3390/diagnostics11071224] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]