3,761 research outputs found
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
Short-segment heart sound classification using an ensemble of deep convolutional neural networks
This paper proposes a framework based on deep convolutional neural networks
(CNNs) for automatic heart sound classification using short-segments of
individual heart beats. We design a 1D-CNN that directly learns features from
raw heart-sound signals, and a 2D-CNN that takes inputs of two- dimensional
time-frequency feature maps based on Mel-frequency cepstral coefficients
(MFCC). We further develop a time-frequency CNN ensemble (TF-ECNN) combining
the 1D-CNN and 2D-CNN based on score-level fusion of the class probabilities.
On the large PhysioNet CinC challenge 2016 database, the proposed CNN models
outperformed traditional classifiers based on support vector machine and hidden
Markov models with various hand-crafted time- and frequency-domain features.
Best classification scores with 89.22% accuracy and 89.94% sensitivity were
achieved by the ECNN, and 91.55% specificity and 88.82% modified accuracy by
the 2D-CNN alone on the test set.Comment: 8 pages, 1 figure, conferenc
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
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