2 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
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