  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%.
