A Federated Learning Framework for Stenosis Detection

• URL: http://arxiv.org/abs/2310.19445v1
• Date: Mon, 30 Oct 2023 11:13:40 GMT
• Title: A Federated Learning Framework for Stenosis Detection
• Authors: Mariachiara Di Cosmo, Giovanna Migliorelli, Matteo Francioni, Andi Mucaj, Alessandro Maolo, Alessandro Aprile, Emanuele Frontoni, Maria Chiara Fiorentino, and Sara Moccia
• Abstract summary: This study explores the use of Federated Learning (FL) for stenosis detection in coronary angiography images (CA) Two heterogeneous datasets from two institutions were considered: dataset 1 includes 1219 images from 200 patients, which we acquired at the Ospedale Riuniti of Ancona (Italy) dataset 2 includes 7492 sequential images from 90 patients from a previous study available in the literature.
• Score: 70.27581181445329
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This study explores the use of Federated Learning (FL) for stenosis detection in coronary angiography images (CA). Two heterogeneous datasets from two institutions were considered: Dataset 1 includes 1219 images from 200 patients, which we acquired at the Ospedale Riuniti of Ancona (Italy); Dataset 2 includes 7492 sequential images from 90 patients from a previous study available in the literature. Stenosis detection was performed by using a Faster R-CNN model. In our FL framework, only the weights of the model backbone were shared among the two client institutions, using Federated Averaging (FedAvg) for weight aggregation. We assessed the performance of stenosis detection using Precision (P rec), Recall (Rec), and F1 score (F1). Our results showed that the FL framework does not substantially affects clients 2 performance, which already achieved good performance with local training; for client 1, instead, FL framework increases the performance with respect to local model of +3.76%, +17.21% and +10.80%, respectively, reaching P rec = 73.56, Rec = 67.01 and F1 = 70.13. With such results, we showed that FL may enable multicentric studies relevant to automatic stenosis detection in CA by addressing data heterogeneity from various institutions, while preserving patient privacy.

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