Deep Learning Classification of Venous Thromboembolism Based on Ultrasound Imaging

A. Olivier (), A. Mansour (), C. Hoffmann, L. Bressollette () and B. Clement ()
Additional contact information
A. Olivier: Lab-STICC UMR 6285 CNRS, ENSTA
A. Mansour: Lab-STICC UMR 6285 CNRS, ENSTA
C. Hoffmann: GETBO UMR 13-04 CHRU Cavale Blanche
L. Bressollette: GETBO UMR 13-04 CHRU Cavale Blanche
B. Clement: Lab-STICC UMR 6285 CNRS, ENSTA

Chapter Chapter 2 in Advances in Data Clustering, 2024, pp 23-41 from Springer

Abstract: Abstract Venous thromboembolism (VTE) occurs when a blood clot forms in a vein. According to the US National Institutes of Health, VTE affects 0.13% of men and around 0.11% of women in the United States every year, i.e., about 400 000 people per year. VTE includes deep vein thrombosis (DVT) and pulmonary embolism (PE). DVT is linked to the obstruction of a deep vein by a blood clot, usually in the lower leg, thigh, or pelvis. Whereas pulmonary embolism (PE) results from the migration of the blood clot toward a pulmonary artery. The objective of our project is to evaluate the possibility of predicting a PE based on ultrasound (US) images. It should be emphasized that there is no medical expertise for the detection of PE from these images. We proposed two methods: the first is based on the extraction of texture descriptors and the second relies on deep learning models. We developed a learning scheme for deep neural networks based on a joint training on a classification and segmentation task, and then a specialization of the network on the classification task. Alternatively, we built a model combining images and clinical data. Beyond the techniques used, significant work has been carried out to sort the database studied and select images. We obtained conclusive accuracy on the detection of PE.

Keywords: Machine learning; Deep learning; Texture analysis; Radiomics; Classification; Multi-supervision; Ultrasound imaging; Gougerot-Sjögren Syndrome (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-97-7679-5_2

Ordering information: This item can be ordered from
http://www.springer.com/9789819776795

DOI: 10.1007/978-981-97-7679-5_2

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().