Trends and statistics of artificial intelligence and radiomics research in Radiology, Nuclear Medicine, and Medical Imaging: bibliometric analysis
Author:
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s00330-023-09772-0.pdf
Reference31 articles.
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2. Cheng PM, Montagnon E, Yamashita R et al (2021) Deep learning: an update for radiologists. Radiographics 41:1427–1445. https://doi.org/10.1148/rg.2021200210
3. Zhang Y, Li H, Zheng Q (2023) A comprehensive characterization of hippocampal feature ensemble serves as individualized brain signature for Alzheimer’s disease: deep learning analysis in 3238 participants worldwide. Eur Radiol. https://doi.org/10.1007/s00330-023-09519-x
4. Kelly B, Martinez M, Do H et al (2023) DEEP MOVEMENT: deep learning of movie files for management of endovascular thrombectomy. Eur Radiol. https://doi.org/10.1007/s00330-023-09478-3
5. Veiga-Canuto D, Cerdà-Alberich L, Jiménez-Pastor A et al (2023) Independent validation of a deep learning nnU-Net tool for neuroblastoma detection and segmentation in MR images. Cancers 15:1622. https://doi.org/10.3390/cancers15051622
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