A meta-learning-based robust federated learning for diagnosing lung adenocarcinoma and tuberculosis granulomas.

ABSTRACT:

Background

Differentiating between lung adenocarcinoma (LAC) and tuberculosis granuloma (TBG) of solitary pulmonary solid nodules (SPSNs) based on CT images alone is a daunting task for clinical diagnosis. Thus, it is crucial to fully utilize CT imaging data to explore effective noninvasive diagnostic methods to improve the identification of TBG and LAC.

Purpose

This study aimed to leverage CT imaging datasets from multiple hospitals for the diagnosis of TBG and LAC in SPSNs. It achieved this by deploying a meta-learning method within a federated learning framework while protecting data privacy.

Methods

A total of 1,026 patients, along with their CT images of solitary pulmonary solid nodules (SPSNs) and corresponding clinical data, were collected from six medical inst

SUBMITTER: Chen Y 

PROVIDER: S-EPMC12510853 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature