What is it about?
Researchers have developed a new method to label chest X-ray images without much manual effort. Instead of doctors marking each image, they use a system that groups similar images together, and only a few of these groups are checked by experts. This approach, tested on two popular datasets, proves almost as accurate as checking every image, but saves time and resources.
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Why is it important?
This study uniquely leverages unsupervised clustering to efficiently label chest X-rays, reducing manual effort. It's the first to use VinDr-CXR and Montgomery datasets for such ground truth generation.
Perspectives
The paper's innovative approach to using unsupervised clustering for CXR image labeling is commendable. Reducing the time and cost of manual labeling while maintaining accuracy is a significant advancement in medical imaging. The novel application of VinDr-CXR and Montgomery datasets further adds to the study's distinctiveness. This methodology could pave the way for more efficient medical image processing in the future.
Mr Victor Ikechukwu Agughasi
Maharaja Institute of Technology
Read the Original
This page is a summary of: Semi-supervised labelling of chest x-ray images using unsupervised clustering for ground-truth generation, Applied Engineering and Technology, September 2023, ASCEE Publications,
DOI: 10.31763/aet.v2i3.1143.
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