Pilot study of machine learning for detection of placenta accreta spectrum-Reference-Cited by-同舟云学术

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Pilot study of machine learning for detection of placenta accreta spectrum

Published:2024-08-07 Issue:3 Volume:64 Page:426-427
ISSN:0960-7692
Container-title:Ultrasound in Obstetrics & Gynecology
language:en
Short-container-title:Ultrasound in Obstet & Gyne

Author:

Zhang Y.¹,Ellestad S. C.²,Gilner J. B.²,Pyne A.²,Boyd B. K.²,Mazurowski M. A.³,Gatta L. A.²^ORCID

Affiliation:

1. Pratt School of Engineering Duke University Durham NC USA

2. Department of Obstetrics and Gynecology Duke University School of Medicine Durham NC USA

3. Mazurowski Lab Duke Center for Artificial Intelligence in Radiology Durham NC USA

Publisher

Wiley

Link

https://obgyn.onlinelibrary.wiley.com/doi/pdf/10.1002/uog.29100

Reference9 articles.

1. Assessment of ultrasound features of placenta accreta spectrum in women at high risk: association with outcome and interobserver concordance

2. Validation of a sonographic checklist for the detection of histologic placenta accreta spectrum

3. ChenLC PapandreouG SchroffF AdamH.Rethinking atrous convolution for semantic image segmentation.2017. doi:10.48550/arXiv.1706.05587

4. RonnebergerO FischerP BroxT.U‐net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer‐Assisted Intervention–MICCAI 2015: 18th International Conference Munich Germany October 5–9 2015 Proceedings Part III 18. Springer International Publishing; 2015:234‐241.

5. Improved U-Nets with inception blocks for building detection

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