A DeBERTa-Base model trained on GPT-4 pseudo-labels for 200k chest X-ray reports reports Macro F1 0.9120 on MIMIC-500, but the 'distillation' loss reduces to ordinary cross-entropy on hard labels.
A systematic review of natural language processing applied to radiology reports,
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High-Fidelity Pseudo-label Generation by Large Language Models for Training Robust Radiology Report Classifiers
A DeBERTa-Base model trained on GPT-4 pseudo-labels for 200k chest X-ray reports reports Macro F1 0.9120 on MIMIC-500, but the 'distillation' loss reduces to ordinary cross-entropy on hard labels.