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Journal ArticleDOI

Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects.

TLDR
This review covers state‐of‐the‐art segmentation and classification methodologies for the whole fetus and, more specifically, the fetal brain, lungs, liver, heart and placenta in magnetic resonance imaging and (3D) ultrasound for the first time.
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This article is published in Medical Image Analysis.The article was published on 2019-01-01. It has received 70 citations till now.

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Citations
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Journal ArticleDOI

CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation

TL;DR: CA-Net as mentioned in this paper proposes a joint spatial attention module to make the network focus more on the foreground region and a novel channel attention module is proposed to adaptively recalibrate channel-wise feature responses and highlight the most relevant feature channels.
Journal ArticleDOI

CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation

TL;DR: This work makes extensive use of multiple attentions in a CNN architecture and proposes a comprehensive attention-based CNN (CA-Net) for more accurate and explainable medical image segmentation that is aware of the most important spatial positions, channels and scales at the same time.
Journal ArticleDOI

A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier

TL;DR: Deep features are extracted from the inceptionv3 model, in which score vector is acquired from softmax and supplied to the quantum variational classifier (QVR) for discrimination between glioma, meningiomas, no tumor, and pituitary tumor to prove the proposed model's effectiveness.
Journal ArticleDOI

RLDS: An explainable residual learning diagnosis system for fetal congenital heart disease

TL;DR: Wang et al. as discussed by the authors proposed a simple yet effective residual learning diagnosis system (RLDS) for diagnosing fetal CHD to improve diagnostic accuracy, which adopts convolutional neural networks to extract discriminative features of the fetal cardiac anatomical structures.
Journal ArticleDOI

Accurate Detection of Septal Defects With Fetal Ultrasonography Images Using Deep Learning-Based Multiclass Instance Segmentation

TL;DR: The results suggest that the model used has a high potential to help cardiologists complete the initial screening for fetal congenital heart disease and a strong correlation between the predicted septal defects and ground truth as a mean average precision (mAP).
References
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Journal ArticleDOI

Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation

TL;DR: An efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data, and improves on the state-of-the‐art for all three applications.
Journal ArticleDOI

Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning

TL;DR: A novel deep learning-based interactive segmentation framework by incorporating CNNs into a bounding box and scribble-based segmentation pipeline and proposing a weighted loss function considering network and interaction-based uncertainty for the fine tuning is proposed.
Journal ArticleDOI

The ultrasonic changes in the maturing placenta and their relation to fetal pulmonic maturity.

TL;DR: A correlation between maturational changes of the placenta as seen by ultrasound and fetal pulmonic maturity as indicated by L/S ratio is suggested.
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