arXiv:2607.06597v1 Announce Type: cross Abstract: Public chest-radiograph (CXR) datasets are typically released with small, fixed label schemas such as CheXpert-14. However, the underlying free-text reports describe far more findings -- and which findings matter depends on the task, site, and reader. We release a pipeline that converts free-text reports into multi-label matrices and then reconfigures the label schema through dictionary edits rather than new inference passes, i.e., without relabeling the corpus. After this one-time pass, reconfiguring MIMIC-CXR (223K reports) from cached annota
The proliferation of medical imaging data and free-text reports has created an urgent need for more flexible and efficient data annotation methods for AI model training.
This development significantly streamlines the process of preparing medical imaging datasets for AI, accelerating development and deployment of diagnostic tools by eliminating repetitive relabeling tasks.
Radiology AI research can now dynamically adapt label schemas to specific tasks or clinical contexts without re-annotating entire datasets, enabling more nuanced and adaptable AI models.
- · AI medical diagnostics developers
- · Healthcare providers adopting AI
- · Researchers using public medical datasets
- · Manual data annotation services for medical imaging
- · AI models reliant on fixed, inflexible label schemas
Faster iteration and deployment of AI models for medical imaging analysis.
Improved accuracy and specificity of AI diagnostics due to tailored labeling.
Potential for new AI-driven insights from previously underutilized aspects of medical reports.
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Read at arXiv cs.CL