4 research outputs found
Automatic ventricle segmentation using CNNs in cardiac MRI
Cardiac magnetic resonance imaging has been proven to be a great aid tool in clinical diagnosis. Computational models arising from these images have been developed for many years by engineers, radiologists and clinicians. A first task in this process is to segment the different regions of the heart, where machine learning and, more recently, deep learning, have shown good performance. My project aims to improve the current network performance when segmenting the left-ventricular, myocardial and right-ventricular regions through (1) data augmentation, (2) data-set combination and (3) loss-function optimization, with a limited amount of computational resources. Results show improvements for all three methodologies. In addition, investing computational resources on muscular regions provides better performance in cavity regions
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
Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review
In recent years, cardiovascular diseases (CVDs) have become one of the
leading causes of mortality globally. CVDs appear with minor symptoms and
progressively get worse. The majority of people experience symptoms such as
exhaustion, shortness of breath, ankle swelling, fluid retention, and other
symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia,
cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina
are the most common CVDs. Clinical methods such as blood tests,
electrocardiography (ECG) signals, and medical imaging are the most effective
methods used for the detection of CVDs. Among the diagnostic methods, cardiac
magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the
disease, plan treatment and predict CVDs. Coupled with all the advantages of
CMR data, CVDs diagnosis is challenging for physicians due to many slices of
data, low contrast, etc. To address these issues, deep learning (DL) techniques
have been employed to the diagnosis of CVDs using CMR data, and much research
is currently being conducted in this field. This review provides an overview of
the studies performed in CVDs detection using CMR images and DL techniques. The
introduction section examined CVDs types, diagnostic methods, and the most
important medical imaging techniques. In the following, investigations to
detect CVDs using CMR images and the most significant DL methods are presented.
Another section discussed the challenges in diagnosing CVDs from CMR data.
Next, the discussion section discusses the results of this review, and future
work in CVDs diagnosis from CMR images and DL techniques are outlined. The most
important findings of this study are presented in the conclusion section
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