A Federated Learning Framework for Stenosis Detection
Mariachiara Di Cosmo Giovanna Migliorelli Matteo Francioni Andi Mucaj Alessandro Maolo Alessandro Aprile Emanuele Frontoni Maria Chiara Fiorentino Sara Moccia
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.