Development and validation of an abnormality-derived deep-learning diagnostic system for major respiratory diseases.

ABSTRACT: Respiratory diseases impose a tremendous global health burden on large patient populations. In this study, we aimed to develop DeepMRD^TR, a deep learning-based medical image interpretation system for the diagnosis of major respiratory diseases based on the automated identification of a wide range of radiological abnormalities through computed tomography (CT) and chest X-ray (CXR) from real-world, large-scale datasets. DeepMRD^TR comprises four networks (two CT-Nets and two CXR-Nets) that exploit contrastive learning to generate pre-training parameters that are fine-tuned on the retrospective dataset collected from a single institution. The performance of DeepMRD^TR was evaluated for abnormality identification and disease diagnosis on data from two different i

SUBMITTER: Wang C 

PROVIDER: S-EPMC9395860 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature