Deep learning–based diagnostic models for bone lesions: is current research ready for clinical translation?-Reference-Cited by-同舟云学术

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Deep learning–based diagnostic models for bone lesions: is current research ready for clinical translation?

Published:2024-01-08 Issue:7 Volume:34 Page:4284-4286
ISSN:1432-1084
Container-title:European Radiology
language:en
Short-container-title:Eur Radiol

Author:

Zhong Jingyu^ORCID

Funder

National Natural Science Foundation of China

Yangfan Project of Science and Technology Commission of Shanghai Municipality

Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00330-023-10555-w.pdf

Reference10 articles.

1. Ribeiro GJ, Gillet R, Blum A, Teixeira PAG (2023) Imaging report and data system (RADS) for bone tumors: where do we stand and future directions. Skeletal Radiol 52(2):151–156

2. He Y, Pan I, Bao B et al (2020) Deep learning-based classification of primary bone tumors on radiographs: a preliminary study. EBioMedicine 62:103121

3. Li J, Li S, Li X et al (2023) Primary bone tumor detection and classification in full-field bone radiographs via YOLO deep learning model. Eur Radiol 33(6):4237–4248

4. von Schacky CE, Wilhelm NJ, Schäfer VS et al (2021) Multitask deep learning for segmentation and classification of primary bone tumors on radiographs. Radiology 301(2):398–406

5. Yildiz Potter I, Yeritsyan D, Mahar S et al (2023) Automated bone tumor segmentation and classification as benign or malignant using computed tomographic imaging. J Digit Imaging 36(3):869–878

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