4 research outputs found
Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks
Cardiovascular disease (CVD) is the leading cause of mortality yet largely
preventable, but the key to prevention is to identify at-risk individuals
before adverse events. For predicting individual CVD risk, carotid intima-media
thickness (CIMT), a noninvasive ultrasound method, has proven to be valuable,
offering several advantages over CT coronary artery calcium score. However,
each CIMT examination includes several ultrasound videos, and interpreting each
of these CIMT videos involves three operations: (1) select three end-diastolic
ultrasound frames (EUF) in the video, (2) localize a region of interest (ROI)
in each selected frame, and (3) trace the lumen-intima interface and the
media-adventitia interface in each ROI to measure CIMT. These operations are
tedious, laborious, and time consuming, a serious limitation that hinders the
widespread utilization of CIMT in clinical practice. To overcome this
limitation, this paper presents a new system to automate CIMT video
interpretation. Our extensive experiments demonstrate that the suggested system
significantly outperforms the state-of-the-art methods. The superior
performance is attributable to our unified framework based on convolutional
neural networks (CNNs) coupled with our informative image representation and
effective post-processing of the CNN outputs, which are uniquely designed for
each of the above three operations.Comment: J. Y. Shin, N. Tajbakhsh, R. T. Hurst, C. B. Kendall, and J. Liang.
Automating carotid intima-media thickness video interpretation with
convolutional neural networks. CVPR 2016, pp 2526-2535; N. Tajbakhsh, J. Y.
Shin, R. T. Hurst, C. B. Kendall, and J. Liang. Automatic interpretation of
CIMT videos using convolutional neural networks. Deep Learning for Medical
Image Analysis, Academic Press, 201
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Sparsely Activated Networks: A new method for decomposing and compressing data
Recent literature on unsupervised learning focused on designing structural
priors with the aim of learning meaningful features, but without considering
the description length of the representations. In this thesis, first we
introduce the{\phi}metric that evaluates unsupervised models based on their
reconstruction accuracy and the degree of compression of their internal
representations. We then present and define two activation functions (Identity,
ReLU) as base of reference and three sparse activation functions (top-k
absolutes, Extrema-Pool indices, Extrema) as candidate structures that minimize
the previously defined metric . We lastly present Sparsely Activated
Networks (SANs) that consist of kernels with shared weights that, during
encoding, are convolved with the input and then passed through a sparse
activation function. During decoding, the same weights are convolved with the
sparse activation map and subsequently the partial reconstructions from each
weight are summed to reconstruct the input. We compare SANs using the five
previously defined activation functions on a variety of datasets (Physionet,
UCI-epilepsy, MNIST, FMNIST) and show that models that are selected using
have small description representation length and consist of
interpretable kernels.Comment: PhD Thesis in Greek, 158 pages for the main text, 23 supplementary
pages for presentation, arXiv:1907.06592, arXiv:1904.13216, arXiv:1902.1112