Collaborative training of medical artificial intelligence models with non-uniform labels.

ABSTRACT: Due to the rapid advancements in recent years, medical image analysis is largely dominated by deep learning (DL). However, building powerful and robust DL models requires training with large multi-party datasets. While multiple stakeholders have provided publicly available datasets, the ways in which these data are labeled vary widely. For Instance, an institution might provide a dataset of chest radiographs containing labels denoting the presence of pneumonia, while another institution might have a focus on determining the presence of metastases in the lung. Training a single AI model utilizing all these data is not feasible with conventional federated learning (FL). This prompts us to propose an extension to the widespread FL process, namely flexible federated learning (FFL) for collabor

SUBMITTER: Tayebi Arasteh S 

PROVIDER: S-EPMC10102221 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature