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Clinical-BERT: Vision-Language Pre-training for Radiograph Diagnosis and Reports Generation

AAAI 2022 · Vision-language and multimodal › Human and embodied modeling · approach: Medical vision-language models · 88 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use Clinical-BERT rather than PPKEDRadiograph report generation · IU X-RayBLEU-1: 0.483 -> 0.495Helped
Use Clinical-BERT rather than ASGKRadiograph report generation · COV-CTRCIDEr: 0.684 -> 1.218Helped
Use Clinical-BERT rather than PPKEDRadiograph report generation · MIMIC-CXRBLEU-1: 0.360 -> 0.383Helped
Use Clinical-BERT rather than DMERadiograph diagnosis · NIH ChestX-ray14mean AUC: 0.8349 -> 0.8450Helped
Add Clinical Diagnosis pre-training (ablation)Clinical-BERT report generation · IU X-RayBLEU-1: 0.465 -> 0.479Helped
Add Masked MeSH Modeling after Clinical Diagnosis (ablation)Clinical-BERT report generation · IU X-RayBLEU-1: 0.479 -> 0.489Helped
Add Image-MeSH Matching after CD and MMM (ablation)Clinical-BERT report generation · MIMIC-CXRBLEU-1: 0.363 -> 0.372Helped
Add sparse attention to Image-MeSH Matching (ablation)Clinical-BERT report generation · MIMIC-CXRBLEU-1: 0.372 -> 0.383Helped
Add sparse attention to Image-MeSH Matching (ablation)Clinical-BERT report generation · IU X-RayROUGE-L: 0.371 -> 0.376Mixed
Increase sparse-attention K from 12 (ablation)Clinical-BERT report generation · MIMIC-CXRBLEU-1: 0.383 -> 0.378Hurt

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