Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results-Reference-Cited by-同舟云学术

Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results

Published:2024-02-17 Issue:4 Volume:49 Page:1194-1201
ISSN:2366-0058
Container-title:Abdominal Radiology
language:en
Short-container-title:Abdom Radiol

Author:

Anari Pouria Yazdian,Lay Nathan,Zahergivar Aryan,Firouzabadi Fatemeh Dehghani,Chaurasia Aditi,Golagha Mahshid,Singh Shiva,Homayounieh Fatemeh,Obiezu Fiona,Harmon Stephanie,Turkbey Evrim,Merino Maria,Jones Elizabeth C.,Ball Mark W.,Linehan W. Marston,Turkbey Baris,Malayeri Ashkan A.^ORCID

Funder

Foundation for the National Institutes of Health

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00261-023-04172-w.pdf

Reference24 articles.

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3. Cooper S, Flood TA, Khodary ME, Shabana WM, Papadatos D, Lavallee LT, et al. Diagnostic Yield and Complication Rate in Percutaneous Needle Biopsy of Renal Hilar Masses With Comparison With Renal Cortical Mass Biopsies in a Cohort of 195 Patients. American Journal of Roentgenology. 2019;212(3):570-5.

4. Cotta BH, Meagher MF, Bradshaw A, Ryan ST, Rivera-Sanfeliz G, Derweesh IH. Percutaneous renal mass biopsy: historical perspective, current status, and future considerations. Expert Review of Anticancer Therapy. 2019;19(4):301-8.

5. Sahni VA, Silverman SG. Biopsy of renal masses: when and why. Cancer imaging : the official publication of the International Cancer Imaging Society. 2009;9(1):44-55.