@inproceedings{9d13a55e80d94d039d3c94f6d43667c3,
title = "Fusion of images and clinical features for the prediction of Pulmonary embolism in Ultrasound imaging",
abstract = "Venous Thromboembolism (VTE) is a life-threatening disease encompassing pulmonary embolism and deep venous thrombosis (DVT). Pulmonary embolism occurs in 50% of patients with a proximal deep venous thrombosis. We aimed to predict the occurrence of a pulmonary embolism in patients with a DVT from clinical data and Ultrasound images of proximal thrombosis. To address this task, we proposed to use a Deep learning model that uses both images and 5 clinical factors as input and we aimed to measure the contributions compared to using only images. Promising results were obtained with both models compared to the state-of-art. The contribution of the clinical factors remains unclear but a gain in accuracy was observed when using smaller models.",
keywords = "Data fusion, Deep learning, Pulmonary embolism, Ultrasound imaging",
author = "Aurelien Olivier and Clement Hoffmann and Ali Mansour and Luc Bressollette and Benoit Clement",
year = "2023",
month = aug,
day = "9",
doi = "10.1109/SSP53291.2023.10208034",
language = "English",
series = "IEEE Workshop on Statistical Signal Processing Proceedings",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "423--427",
booktitle = "Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023",
address = "United States",
note = "22nd IEEE Statistical Signal Processing Workshop, SSP 2023 ; Conference date: 02-07-2023 Through 05-07-2023",
}