Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automatic Detection of B-lines in Lung Ultrasound Videos From Severe
  Dengue Patients   Lung ultrasound (LUS) imaging is used to assess lung abnormalities, including
the presence of B-line artefacts due to fluid leakage into the lungs caused by
a variety of diseases. However, manual detection of these artefacts is
challenging. In this paper, we propose a novel methodology to automatically
detect and localize B-lines in LUS videos using deep neural networks trained
with weak labels. To this end, we combine a convolutional neural network (CNN)
with a long short-term memory (LSTM) network and a temporal attention
mechanism. Four different models are compared using data from 60 patients.
Results show that our best model can determine whether one-second clips contain
B-lines or not with an F1 score of 0.81, and extracts a representative frame
with B-lines with an accuracy of 87.5%.

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The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
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The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
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The categories of this paper are: chem-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
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The categories of this paper are: hep-th
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The categories of this paper are: bayes-an
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The categories of this paper are: nlin
14
The categories of this paper are: math-ph