ArticleScientific reports2015
Discriminative Learning for Automatic Staging of Placental Maturity via Multi-layer Fisher Vector.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes.Briefings in bioinformatics · 2021Pooled it
- Placental MRI texture features in prenatal exposure to opioids and tobacco.Scientific reports · 2025Article
- Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology.Journal of clinical medicine · 2023Review
- Artificial Intelligence in Prenatal Ultrasound Diagnosis.Frontiers in medicine · 2021Review
- Longitudinal Analysis for Disease Progression via Simultaneous Multi-Relational Temporal-Fused Learning.Frontiers in aging neuroscience · 2017Article
- Discriminative Learning for Alzheimer's Disease Diagnosis via Canonical Correlation Analysis and Multimodal Fusion.Frontiers in aging neuroscience · 2016Article
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Authors and funding
7 authors.
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Abstract
Currently, placental maturity is performed using subjective evaluation, which can be unreliable as it is highly dependent on the observations and experiences of clinicians. To address this problem, this paper proposes a method to automatically stage placenta maturity from B-mode ultrasound (US) images based on dense sampling and novel feature descriptors. Specifically, our proposed method first densely extracts features with a regular grid based on dense sampling instead of a few unreliable interest points. Followed by, these features are clustered using generative Gaussian mixture model (GMM) to obtain high order statistics of the features. The clustering representatives (i.e., cluster means) are encoded by Fisher vector (FV) for staging accuracy enhancement. Differing from the previous studies, a multi-layer FV is investigated to exploit the spatial information rather than the single layer FV. Experimental results show that the proposed method with the dense FV has achieved an area under the receiver of characteristics (AUC) of 96.77%, sensitivity and specificity of 98.04% and 93.75% for the placental maturity staging, respectively. Our experimental results also demonstrate that the dense feature outperforms the traditional sparse feature for placental maturity staging.
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