ArticleScientific reports2021
Evaluation of transfer learning in deep convolutional neural network models for cardiac short axis slice classification.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Detection of hypoplastic left heart syndrome anatomy from cardiovascular magnetic resonance images using machine learning.Magma (New York, N.Y.) · 2024Article
- Transfer Learning Approach and Nucleus Segmentation with MedCLNet Colon Cancer Database.Journal of digital imaging · 2023Article
- Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine.Computational and structural biotechnology journal · 2023Review
- Prediction of Short-Term Mortality of Cardiac Care Unit Patients Using Image-Transformed ECG Waveforms.IEEE journal of translational engineering in health and medicine · 2023Article
- Estimation of Cardiac Short Axis Slice Levels with a Cascaded Deep Convolutional and Recurrent Neural Network Model.Tomography (Ann Arbor, Mich.) · 2022Article
- Wearable Sensors for Activity Recognition in Ultimate Frisbee Using Convolutional Neural Networks and Transfer Learning.Sensors (Basel, Switzerland) · 2022Article
- Classification of Gliomas and Germinomas of the Basal Ganglia by Transfer Learning.Frontiers in oncology · 2022Article
- Classification of Cardiomyopathies from MR Cine Images Using Convolutional Neural Network with Transfer Learning.Diagnostics (Basel, Switzerland) · 2021Article
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2 authors.
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Abstract
In computer-aided analysis of cardiac MRI data, segmentations of the left ventricle (LV) and myocardium are performed to quantify LV ejection fraction and LV mass, and they are performed after the identification of a short axis slice coverage, where automatic classification of the slice range of interest is preferable. Standard cardiac image post-processing guidelines indicate the importance of the correct identification of a short axis slice range for accurate quantification. We investigated the feasibility of applying transfer learning of deep convolutional neural networks (CNNs) as a means to automatically classify the short axis slice range, as transfer learning is well suited to medical image data where labeled data is scarce and expensive to obtain. The short axis slice images were classified into out-of-apical, apical-to-basal, and out-of-basal, on the basis of short axis slice location in the LV. We developed a custom user interface to conveniently label image slices into one of the three categories for the generation of training data and evaluated the performance of transfer learning in nine popular deep CNNs. Evaluation with unseen test data indicated that among the CNNs the fine-tuned VGG16 produced the highest values in all evaluation categories considered and appeared to be the most appropriate choice for the cardiac slice range classification.
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