Fully convolutional multi‐scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers
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Summary
The approach combined both cardiac segmentation and disease diagnosis into a fully automated framework which is computationally efficient and hence has the potential to be incorporated in computer‐aided diagnosis (CAD) tools for clinical application.
- Type
- preprint
- Published
- 2018-01-16
- Cited by
- 352
- References
- 107
- Access
- Open access
- OpenAlex
- https://openalex.org/W2782881426
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:42980635
Keywords
Computer science, Artificial intelligence, Segmentation, Convolutional neural network, Jaccard index
References
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- Comprehensive Segmentation of Cine Cardiac MR Images
- Automated method for detection and segmentation of liver metastatic lesions in follow-up CT examinations
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- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Brain tumor segmentation with Deep Neural Networks
- Fully convolutional networks for semantic segmentation
- Congenital Aortic Disease: 4D Magnetic Resonance Segmentation and Quantitative Analysis
- Automatic cardiac ventricle segmentation in MR images: a validation study
- Unsupervised 4D myocardium segmentation with a Markov Random Field based deformable model
- Time Continuous Tracking and Segmentation of Cardiovascular Magnetic Resonance Images Using Multidimensional Dynamic Programming
- In-line automated tracking for ventricular function with magnetic resonance imaging.
- The role of cardiovascular magnetic resonance imaging in heart failure.
- A review of segmentation methods in short axis cardiac MR images
- Fast left ventricle tracking in CMR images using localized anatomical affine optical flow
- Computer-aided diagnosis via model-based shape analysis: cardiac MR and echo
- Automatic image‐driven segmentation of the ventricles in cardiac cine MRI
- A statistical shape model of the heart and its application to model-based segmentation
Cited by
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- Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach
- Multi-Scale Fully Convolutional Network for Cardiac Left Ventricle Segmentation
- Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow
- Thyroid Diagnosis from SPECT Images Using Convolutional Neural Network with Optimization
- Unsupervised shape and motion analysis of 3822 cardiac 4D MRIs of UK Biobank
- Segmentation of roots in soil with U-Net
- Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap a Novel Molecular-Image-Input Technique for Quantitative Structure–Activity Relationship (QSAR) Analysis
- Coronary Arteries Segmentation Based on 3D FCN With Attention Gate and Level Set Function
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- Myocardium Detection by Deep SSAE Feature and Within-Class Neighborhood Preserved Support Vector Classifier and Regressor
- PsLSNet: Automated psoriasis skin lesion segmentation using modified U-Net-based fully convolutional network
- Cerebral Micro-Bleeding Detection Based on Densely Connected Neural Network
- Accurate colorectal tumor segmentation for CT scans based on the label assignment generative adversarial network.
- Improving the Generalizability of Convolutional Neural Network-Based Segmentation on CMR Images
- Recurrent Attention Mechanism Networks for Enhanced Classification of Biomedical Images
- Fully Automatic Segmentation Of Short-Axis Cardiac MRI Using Modified Deep Layer Aggregation
- Multi-Scale Inception Based Super-Resolution Using Deep Learning Approach
- Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction
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