A few-shot learning framework for the diagnosis of osteopenia and osteoporosis using knee X-ray images.

ABSTRACT:

Objective

We developed a few-shot learning (FSL) framework for the diagnosis of osteopenia and osteoporosis in knee X-ray images.

Methods

Computer vision models containing deep convolutional neural networks were fine-tuned to enable generalization from natural images (ImageNet) to chest X-ray images (normal vs. pneumonia, base images). Then, a series of automated machine learning classifiers based on the Euclidean distances of base images were developed to make predictions for novel images (normal vs. osteopenia vs. osteoporosis). The performance of the FSL framework was compared with that of junior and senior radiologists. In addition, the gradient-weighted class activation mapping algorithm was used for visual interpretation.

Results

In Cohort #1, the mean accuracy

SUBMITTER: Xie H 

PROVIDER: S-EPMC11375658 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature