Journal ArticleDOI
Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset
TLDR
A stacked DPN (S-DPN) algorithm is proposed to further improve the representation performance of the original DPN, and S-DPn is applied to the task of texture feature learning for ultrasound based tumor classification with small dataset, suggesting that S- DPN can be a strong candidate for the texture feature representation learning on small ultrasound datasets.About:
This article is published in Neurocomputing.The article was published on 2016-06-19. It has received 151 citations till now. The article focuses on the topics: Feature learning & Feature (computer vision).read more
Citations
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GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification
TL;DR: It is shown that generated medical images can be used for synthetic data augmentation, and improve the performance of CNN for medical image classification, and generalize to other medical classification applications and thus support radiologists’ efforts to improve diagnosis.
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Deep Learning in Medical Ultrasound Analysis: A Review
TL;DR: Several popular deep learning architectures are briefly introduced, and their applications in various specific tasks in US image analysis, such as classification, detection, and segmentation are discussed.
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Dynamic functional network connectivity in idiopathic generalized epilepsy with generalized tonic–clonic seizure
TL;DR: The results revealed that state‐specific FNC disruptions were observed in IGE‐GTCS and the majority of aberrant functional connectivity manifested itself in default mode network and suggested that the dynamic FNC analysis was a promising avenue to deepen the understanding of this disease.
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FUIQA: Fetal Ultrasound Image Quality Assessment With Deep Convolutional Networks
TL;DR: It will be illustrated that the computerized assessment with the FUIQA scheme can be comparable to the subjective ratings from medical doctors.
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Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey.
TL;DR: This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years and introduced the major feature and the classifier employed by the traditional ultrasound CAD and the deep learning ultrasound CAD.
References
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Representation Learning: A Review and New Perspectives
TL;DR: Recent work in the area of unsupervised feature learning and deep learning is reviewed, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks.