Weakly Supervised Learning of Placental Ultrasound Images with Residual Networks
Open accessAbstract
Accurate classification and localization of anatomical structures in images is a precursor for fully automatic image-based diagnosis of placental abnormalities. For placental ultrasound images, typically acquired in clinical screening and risk assessment clinics, these structures can have quite indistinct boundaries and low contrast, and image-level interpretation is a challenging and time-consuming task even for experienced clinicians. In this paper, we propose an automatic classification model for anatomy recognition in placental ultrasound images. We employ deep residual networks to effectively learn discriminative features in an end-to-end fashion. Experimental results on a large placental ultra-sound image database (10,808 distinct 2D image patches from 60 placental ultrasound volumes) demonstrate that the proposed network architecture design achieves a very high recognition accuracy (0.086 top-1 error rate) and provides good localization for complex anatomical structures around the placenta in a weakly supervised fashion. To our knowledge this is the first successful demonstration of multi-structure detection in placental ultrasound images.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Qi et al. reported successful automated localization of the placental lacunae in 2D images as a potential tool for screening abnormally invasive placenta.
“while Qi et al. reported successful automated localization of the placental lacunae in 2D images as a potential tool for screening abnormally invasive placenta [114,115].”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [115]
The Introduction reports successful localization in 2D placental ultrasound images, but identifies the localized structures as the placenta, myometrium, and subcutaneous tissue—not placental lacunae. It discusses abnormally invasive placentation as clinical context; it does not report lacuna localization or demonstrate a screening tool for that condition. (LLM, conf=0.89)
Experimental — accuracy not yet measuredQi et al. proposed a weakly-supervised CNN that achieved high recognition accuracy and could localize complex anatomical structures around the placenta.
“Experimental results demonstrated that the proposed method achieved high recognition accuracy, and could localize complex anatomical structures around the placenta.”
From Deep Learning Strategies for Ultrasound in Pregnancy (2020) · cites this paper as [27]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 17 · Cited by 17
Research works printed but not linked: 1
- [2]Chen H, Dou Q, Yu L, Heng PA: Voxresnet: Deep voxelwise residual networks for volumetric brain segmentation. arXiv preprint arXiv:1608.05895 (2016) 28445774 · arXiv
- Authors
- Qi, H., Collins, S., & Noble, A.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Qi, Huan
- ORCID
- Collins, Sally
- Venue
- Communications in computer and information science
- Publisher
- Springer International Publishing
- Published
- 2017
- Volume
- 723
- Pages
- 98-108
- ISSN
- 1865-0929 (ISSN-L)
- 1865-0937
- PMC
- PMC6816799
- OpenAlex
- W2714188022
- Cited by
- 17
- References
- 17
- Citations by year
- 2025: 1 · 2023: 3 · 2022: 3 · 2021: 3 · 2020: 2 · 2019: 1 · 2018: 4
- FWCI
- 0.55
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Communications in computer and information science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (U01HD087209)
- 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
- placental ultrasound, weakly supervised learning, ResNet, anatomical structure localization, placental abnormalities, end-to-end classification, diagnostic imaging, prenatal screening
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
