31,657 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
Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation
Automatic brain tumor segmentation plays an important role for diagnosis,
surgical planning and treatment assessment of brain tumors. Deep convolutional
neural networks (CNNs) have been widely used for this task. Due to the
relatively small data set for training, data augmentation at training time has
been commonly used for better performance of CNNs. Recent works also
demonstrated the usefulness of using augmentation at test time, in addition to
training time, for achieving more robust predictions. We investigate how
test-time augmentation can improve CNNs' performance for brain tumor
segmentation. We used different underpinning network structures and augmented
the image by 3D rotation, flipping, scaling and adding random noise at both
training and test time. Experiments with BraTS 2018 training and validation set
show that test-time augmentation helps to improve the brain tumor segmentation
accuracy and obtain uncertainty estimation of the segmentation results.Comment: 12 pages, 3 figures, MICCAI BrainLes 201
Joint Prediction of Depths, Normals and Surface Curvature from RGB Images using CNNs
Understanding the 3D structure of a scene is of vital importance, when it
comes to developing fully autonomous robots. To this end, we present a novel
deep learning based framework that estimates depth, surface normals and surface
curvature by only using a single RGB image. To the best of our knowledge this
is the first work to estimate surface curvature from colour using a machine
learning approach. Additionally, we demonstrate that by tuning the network to
infer well designed features, such as surface curvature, we can achieve
improved performance at estimating depth and normals.This indicates that
network guidance is still a useful aspect of designing and training a neural
network. We run extensive experiments where the network is trained to infer
different tasks while the model capacity is kept constant resulting in
different feature maps based on the tasks at hand. We outperform the previous
state-of-the-art benchmarks which jointly estimate depths and surface normals
while predicting surface curvature in parallel
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