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arXiv · 2607.06597

Reconfigurable Radiology Labels Without Relabeling

Jean-Benoit Delbrouck·Dave Van Veen·Akash Pattnaik·Kalina Slavkova·Javid Abderezaei·Harris Bergman·Khan Siddiqui
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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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Jean-Benoit Delbrouck, Dave Van Veen, Akash Pattnaik, Kalina Slavkova, Javid Abderezaei, Harris Bergman, Khan Siddiqui. 2026-07-14. Reconfigurable Radiology Labels Without Relabeling. https://arxiv.org/abs/2607.06597

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