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
More filters
Journal ArticleDOI
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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
More filters
Proceedings Article
Greedy Layer-Wise Training of Deep Networks
TL;DR: These experiments confirm the hypothesis that the greedy layer-wise unsupervised training strategy mostly helps the optimization, by initializing weights in a region near a good local minimum, giving rise to internal distributed representations that are high-level abstractions of the input, bringing better generalization.
Journal ArticleDOI
Orthogonal least squares methods and their application to non-linear system identification
TL;DR: Identification algorithms based on the well-known linear least squares methods of gaussian elimination, Cholesky decomposition, classical Gram-Schmidt, modified Gram- Schmidt, Householder transformation, Givens method, and singular value decomposition are reviewed.
Proceedings Article
Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images
TL;DR: This work addresses a central problem of neuroanatomy, namely, the automatic segmentation of neuronal structures depicted in stacks of electron microscopy images, using a special type of deep artificial neural network as a pixel classifier to segment biological neuron membranes.
Greedy Layer-Wise Training of Deep Networks
TL;DR: These experiments confirm the hypothesis that the greedy layer-wise unsupervised training strategy mostly helps the optimization, by initializing weights in a region near a good local minimum, giving rise to internal distributed representations that are high-level abstractions of the input, bringing better generalization.
Journal ArticleDOI
Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]
TL;DR: An overview of the mainstream deep learning approaches and research directions proposed over the past decade is provided and some perspective into how it may evolve is presented.