371 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
Domain and Geometry Agnostic CNNs for Left Atrium Segmentation in 3D Ultrasound
Segmentation of the left atrium and deriving its size can help to predict and
detect various cardiovascular conditions. Automation of this process in 3D
Ultrasound image data is desirable, since manual delineations are
time-consuming, challenging and observer-dependent. Convolutional neural
networks have made improvements in computer vision and in medical image
analysis. They have successfully been applied to segmentation tasks and were
extended to work on volumetric data. In this paper we introduce a combined
deep-learning based approach on volumetric segmentation in Ultrasound
acquisitions with incorporation of prior knowledge about left atrial shape and
imaging device. The results show, that including a shape prior helps the domain
adaptation and the accuracy of segmentation is further increased with
adversarial learning
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