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Congress: ECR24

Poster Number: C-23452

Type: EPOS Radiographer (scientific)

Authorblock: M. Koshizuka¹, H. Watanabe¹, Y. Takeda¹, Y. Ezawa¹, K. Saito², N. Hayashi¹, M. Sato¹, T. Ogura¹, M. Shimosegawa¹; ¹Maebashi/JP, ²Saitama/JP

Disclosures:

Momoko Koshizuka: Nothing to disclose

Haruyuki Watanabe: Nothing to disclose

Yuto Takeda: Nothing to disclose

Yuina Ezawa: Nothing to disclose

Kazuho Saito: Nothing to disclose

Norio Hayashi: Nothing to disclose

Mitsuru Sato: Nothing to disclose

Toshihiro Ogura: Nothing to disclose

Masayuki Shimosegawa: Nothing to disclose

Keywords: Breast, Mammography, Technology assessment, Image verification

References

[1] Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2021. CA Cancer J. Clin. 2021;71:7–33.

[2] Independent UK Panel on Breast Cancer Screening The benefits and harms of breast cancer screening: An independent review. Lancet. 2012;380:1778–1786.

[3] Programme, NBS Guidance for Breast Screening Mammographers 3rd ed. Public Health England, UK, 2017.

[4] EUREF. European Guidelines for Quality Assurance in Breast Cancer Screening and Diagnosis 4th ed. European Reference Organisation for Quality Assured Breast Screening and Diagnostic Services, 2006.

[5] Watanabe, H., Hayashi, S., Kondo, Y. et al. Quality control system for mammographic breast positioning using deep learning. Sci Rep 13, 7066, 2023.

[6] Lee RS, Gimenez F, Hoogi A, Rubin D. Curated breast imaging subset of DDSM. Cancer Imaging Arch. 2016;6:66.

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