Capability and reliability of deep learning models to make density predictions on low-dose mammograms-Reference-Cited by-同舟云学术

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Capability and reliability of deep learning models to make density predictions on low-dose mammograms

Published:2024-08-06 Issue:04 Volume:11 Page:
ISSN:2329-4302
Container-title:Journal of Medical Imaging
language:
Short-container-title:J. Med. Imag.

Author:

Squires Steven¹,Mackenzie Alistair²,Evans Dafydd Gareth³,Howell Sacha J.⁴,Astley Susan M.¹

Affiliation:

1. University of Manchester, School of Health Sciences, Division of Imaging, Informatics and Data Sciences, Faculty of Biology, Medicine and Health, Manchester, United Kingdom

2. NCCPM, Royal Surrey NHS Foundation Trust, Guildford, United Kingdom

3. University of Manchester, School of Biological Sciences, Division of Evolution, Infection and Genomics, Manchester, Greater Manchester, United Kingdom

4. University of Manchester, Division of Cancer Sciences, Manchester, United Kingdom

Publisher

SPIE-Intl Soc Optical Eng

Reference22 articles.

1. Mammographic surveillance in women aged 35–39 at enhanced familial risk of breast cancer (FH02)

2. Accuracy of self-reported family history of cancer, mutation status and tumor characteristics in patients with early onset breast cancer

3. Biology and Etiology of Young-Onset Breast Cancers among Premenopausal African American Women: Results from the AMBER Consortium

4. Radiation doses in the UK trial of breast screening in women aged 40–48 years

5. Feasibility and acceptability of offering breast cancer risk assessment to general population women aged 30–39 years: a mixed-methods study protocol

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