2,094 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
The Verbal and Non Verbal Signals of Depression -- Combining Acoustics, Text and Visuals for Estimating Depression Level
Depression is a serious medical condition that is suffered by a large number
of people around the world. It significantly affects the way one feels, causing
a persistent lowering of mood. In this paper, we propose a novel
attention-based deep neural network which facilitates the fusion of various
modalities. We use this network to regress the depression level. Acoustic, text
and visual modalities have been used to train our proposed network. Various
experiments have been carried out on the benchmark dataset, namely, Distress
Analysis Interview Corpus - a Wizard of Oz (DAIC-WOZ). From the results, we
empirically justify that the fusion of all three modalities helps in giving the
most accurate estimation of depression level. Our proposed approach outperforms
the state-of-the-art by 7.17% on root mean squared error (RMSE) and 8.08% on
mean absolute error (MAE).Comment: 10 pages including references, 2 figure
Personalized Automatic Estimation of Self-reported Pain Intensity from Facial Expressions
Pain is a personal, subjective experience that is commonly evaluated through
visual analog scales (VAS). While this is often convenient and useful,
automatic pain detection systems can reduce pain score acquisition efforts in
large-scale studies by estimating it directly from the participants' facial
expressions. In this paper, we propose a novel two-stage learning approach for
VAS estimation: first, our algorithm employs Recurrent Neural Networks (RNNs)
to automatically estimate Prkachin and Solomon Pain Intensity (PSPI) levels
from face images. The estimated scores are then fed into the personalized
Hidden Conditional Random Fields (HCRFs), used to estimate the VAS, provided by
each person. Personalization of the model is performed using a newly introduced
facial expressiveness score, unique for each person. To the best of our
knowledge, this is the first approach to automatically estimate VAS from face
images. We show the benefits of the proposed personalized over traditional
non-personalized approach on a benchmark dataset for pain analysis from face
images.Comment: Computer Vision and Pattern Recognition Conference, The 1st
International Workshop on Deep Affective Learning and Context Modelin
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