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Cited by 11Medical Image Segmentation TechniquesAdvanced Neural Network ApplicationsArtificial Intelligence in Healthcare and Education

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

ARVBNet: Real-Time Detection of Anatomical Structures in Fetal Ultrasound Cardiac Four-Chamber Planes

Dong, Jinbao, Liu, Shengfeng, Wang, Tianfu·2019·Lecture Notes in Computer Science
DOI 10.1007/978-3-030-33327-0_16
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Overview

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Registry

Crossref · 10.1007/978-3-030-33327-0_16

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Dong et al. demonstrated a mean average precision of 94.52% for quality control of the four-chamber view.

    “Three studies proposed automated systems for detection and evaluation of the fetal four-chamber view whereas Dong et al. proposed a deep learning framework for quality control of the four-chamber view, demonstrating a mean average precision of 94.52%.80-83”

    From Artificial intelligence in obstetric ultrasound: A scoping review. (2023) · cites this paper as [82]

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 13

  1. Liu, S., Wang, Y., Yang, X., Lei, B., Liu, L., Li, S.X., et al.: Deep learning in medical ultrasound analysis: a review. Engineering 5(2), 261–275 (2019) · DOI · Paper page
  2. Ville, Y.: ‘Ceci n’est pas une échographie’: a plea for quality assessment in prenatal ultrasound. Ultrasound Obstet. Gynecol. 31(1), 1–5 (2008) · DOI
  3. Maraci, M.A., Bridge, C.P., Napolitano, R., Papageorghiou, A., Noble, J.A.: A framework for analysis of linear ultrasound videos to detect fetal presentation and heartbeat. Med. Image Anal. 37, 22–36 (2017) · DOI
  4. Wandell, B.A., Winawer, J.: Computational neuroimaging and population receptive fields. Trends Cogn. Sci. 19(6), 349–357 (2015) · DOI
  5. Liu, S., Huang, D., Wang, Y.: Receptive field block net for accurate and fast object detection. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 404–419. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01252-6_24 · DOI · Paper page
  6. Hu, P., Ramanan, D.: Finding tiny faces. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 951–959 (2017) · DOI
  7. Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2 · DOI · Paper page
  8. Li, Z., Zhou, F.: FSSD: feature fusion single shot multibox detector. arXiv preprint arXiv:1712.00960 (2017) · arXiv
  9. Dai, J., Li, Y., He, K., Sun, J.: R-FCN: object detection via region-based fully convolutional networks. In: Advances in Neural Information Processing Systems, pp. 379–387 (2016)
  10. Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017) · DOI · Paper page
  11. Jeong, J., Park, H., Kwak, N.: Enhancement of SSD by concatenating feature maps for object detection. arXiv preprint arXiv:1705.09587 (2017) · DOI
  12. Fu, C.-Y., Liu, W., Ranga, A, Tyagi, A, Berg, A.C.: DSSD: deconvolutional single shot detector. arXiv preprint arXiv:1701.06659 (2017) · arXiv
  13. Shen, Z., Liu, Z., Li, J., Jiang, Y., Chen, Y., Xue, X.: DSOD: learning deeply supervised object detectors from scratch. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1937–1945 (2017) · DOI

Cited by

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Metadata

Authors
Dong, J., Liu, S., & Wang, T.
Affiliations
Shenzhen University (CN)
Corresponding author
Liu, Shengfeng
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2019
Pages
130-137
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-030-33327-0_16
OpenAlex
W2980087876
Cited by
11
References
3
Citations by year
2025: 2 · 2024: 4 · 2023: 2 · 2022: 3
FWCI
0.24
Type
conference paper
Language
en
Open access
closed
Licence
Locations
Lecture notes in computer science (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
prenatal ultrasound, anatomical structure detection, real-time detection, prenatal diagnosis

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

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