Combining Multiple Style Transfer Networks and Transfer Learning For LGE-CMR Segmentation

Fang, B.; Chen, Junxin; Wang, Wei; Zhou, Yicong

doi:10.1109/icassp43922.2022.9746034

Cited by 3 publications

(2 citation statements)

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“…Recently, style transfer-based methods have been used to synthesize images for data augmentation. 16 In general, the data augmentation-based methods are the most intuitive method to solve the learning problems under small samples. The pseudodata generated through data augmentation merged in small samples are used to train the deep learning networks.…”

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“…Deep learning‐based methods 13‐15 are also used to learn patterns between complex classes and this pattern is applied to small samples to expand the dataset. Recently, style transfer‐based methods have been used to synthesize images for data augmentation 16 . In general, the data augmentation‐based methods are the most intuitive method to solve the learning problems under small samples.…”

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confidence: 99%

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Vocal Cord Leukoplakia Classification Using Siamese Network Under Small Samples of White Light Endoscopy Images

You,

Han,

Shi

et al. 2023

Otolaryngol.--head neck surg.

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ObjectiveAccurate vocal cord leukoplakia classification is instructive for clinical diagnosis and surgical treatment. This article introduces a reliable very deep Siamese network for accurate vocal cord leukoplakia classification.Study DesignA study of a classification network based on a retrospective database.SettingAcademic university and hospital.MethodsThe white light image datasets of vocal cord leukoplakia used in this article were classified into 6 classes: normal tissues, inflammatory keratosis, mild dysplasia, moderate dysplasia, severe dysplasia, and squamous cell carcinoma. The classification performance was assessed by comparing it with 6 classical deep learning models, including AlexNet, VGG Net, Google Inception, ResNet, DenseNet, and Vision Transformer.ResultsExperiments show the superior classification performance of our proposed network compared to state‐of‐the‐art methods. The overall accuracy is 0.9756. The values of sensitivity and specificity are very high as well. The confusion matrix provides information for the 6‐class classification task and demonstrates the superiority of our proposed network.ConclusionOur very deep Siamese network can provide accurate classification results of vocal cord leukoplakia, which facilitates early detection, clinical diagnosis, and surgical treatment. The excellent performance obtained in white light images can reduce the cost for patients, especially those living in developing countries.

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