Converting free-text radiology reports into structured labels supports cohort building, quality assurance, and monitoring of clinical imaging models, but the strongest label extractors are hosted proprietary models whose use raises privacy, cost, and reproducibility concerns. We asked whether a fine-tuned open-weight m...
Aawez Mansuri, Kush Mehta, Mohammadreza Chavoshi et al.· 0 citations
TopKSigLIP outperforms existing open-source mammography and general medical VLMs on both internal and external benchmarks on density assessment, BI-RADS classification, finding subtyping, and cancer prediction under zero-shot evaluation.
Y. Jeon, Beatrice Brown-Mulry, R. Isaac et al.· 0 citations
Training deep learning models on radiological images requires integrating heterogeneous datasets across different sources, file formats, directory layouts, label schemas, and annotation types. We present RadHarmony, an open-source Python library that provides a unified API for loading, harmonizing, and augmenting radio...
Frank Li, Bardia Khosravi, Mohammadreza Chavoshi et al.· arXiv.org· 0 citations
Vision-Language Models (VLMs) for radiology report generation are typically trained on retrospective clinical reports, which suffer from omission noise: clinically present findings are left unreported due to the omission of subtle findings. For example, prior studies show that cardiomegaly may be omitted from ICU chest...
Y. Kobayashi, P. Ramesh, Muhammad Ahmed Chaudhry et al.· 0 citations
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