Federated Learning on Distributed Medical Records for Detection of Lung Nodules

Pragati Baheti, Mukul Sikka, K. V. Arya, R. Rajesh

2020

Abstract

In this work, the concept of federated Learning is applied on medical records of CT scans images for detection of pulmonary lung nodules. Instead of using the naive ways, the authors have come up with decentralizing the training technique by bringing the model to the data rather than accumulating the data at a central place and thus maintaining differential privacy of the records. The training on distributed electronic medical records includes two models: detection of location of nodules and its confirmation. The experiments have been carried out on CT scan images from LIDC dataset and the results shows that the proposed method outperformed the existing methods in terms of detection accuracy.

Paper Citation

in Bibtex Style

@conference{visapp20,
author={Pragati Baheti and Mukul Sikka and K. V. Arya and R. Rajesh},
title={Federated Learning on Distributed Medical Records for Detection of Lung Nodules},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP},
year={2020},
pages={445-451},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009144704450451},
isbn={978-989-758-402-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP
TI - Federated Learning on Distributed Medical Records for Detection of Lung Nodules
SN - 978-989-758-402-2
AU - Baheti P.
AU - Sikka M.
AU - Arya K.
AU - Rajesh R.
PY - 2020
SP - 445
EP - 451
DO - 10.5220/0009144704450451
PB - SciTePress

in Harvard Style

Baheti P., Sikka M., Arya K. and Rajesh R. (2020). Federated Learning on Distributed Medical Records for Detection of Lung Nodules. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP; ISBN 978-989-758-402-2, SciTePress, pages 445-451. DOI: 10.5220/0009144704450451