Reconfigurable Radiology Labels Without Relabeling

• URL: http://arxiv.org/abs/2607.06597v2
• Date: Tue, 14 Jul 2026 21:21:35 GMT
• Title: Reconfigurable Radiology Labels Without Relabeling
• Abstract summary: We release a pipeline that converts free-text reports into multi-label matrices.<n>We show that 43% of CXR studies contain at least one finding outside CheXpert-14.
• Score: 4.19448785173794
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 annotations takes 196 seconds with no API cost, compared to \$6.6K for an equivalent relabeling pass with Claude Opus 4.7. Using a 58-label taxonomy, we show that 43\% of CXR studies contain at least one finding outside CheXpert-14. Image probes trained on these labels match CheXpert-14 probes on shared targets while also reaching 0.78 AUROC on expert-reviewed long-tail labels that CheXpert-14 cannot represent. These results suggest a different unit of work for radiology labeling: once reports are structured, the label schema becomes a configuration to edit, not a corpus to relabel.

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