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- W4287348682 abstract "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%." @default.
- W4287348682 created "2022-07-25" @default.
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- W4287348682 date "2021-02-01" @default.
- W4287348682 modified "2023-09-26" @default.
- W4287348682 title "Automatic Detection of B-lines in Lung Ultrasound Videos From Severe Dengue Patients" @default.
- W4287348682 doi "https://doi.org/10.48550/arxiv.2102.01059" @default.
- W4287348682 hasPublicationYear "2021" @default.
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