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Auto-Encoding Knowledge Graph for Unsupervised Medical Report Generation

NeurIPS 2021 · Image generation › Autoregressive and latent generation · approach: Synthetic training data from generators · 64 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add shared mapping function F in the encoder (ablation)KGAE unsupervised · IU X-rayBLEU-4: 0.086 -> 0.109Helped
add shared mapping function F in the encoder (ablation)KGAE unsupervised · MIMIC-CXRBLEU-4: 0.032 -> 0.045Helped
add knowledge bank (10,000) to decoder (ablation)KGAE unsupervised · IU X-rayBLEU-4: 0.109 -> 0.126Helped
add knowledge bank (10,000) to decoder (ablation)KGAE unsupervised · MIMIC-CXRBLEU-4: 0.045 -> 0.062Helped
add shared mapping function F in the encoder (ablation)KGAE-Supervised · IU X-rayBLEU-4: 0.166 -> 0.162Hurt
add shared mapping function F in the encoder (ablation)KGAE-Supervised · MIMIC-CXRBLEU-4: 0.104 -> 0.101No clear effect
add knowledge bank (10,000) to decoder (ablation)KGAE-Supervised · MIMIC-CXRBLEU-4: 0.101 -> 0.118Helped
increase knowledge bank from 10,000 to 15,000 (ablation)KGAE-Supervised · IU X-rayBLEU-4: 0.179 -> 0.170Hurt
increase knowledge bank from 10,000 to 15,000 (ablation)KGAE unsupervised · MIMIC-CXRBLEU-4: 0.062 -> 0.059Hurt
build knowledge graph from IU X-Ray only instead of MIMIC-CXR (ablation)KGAE unsupervised · IU X-rayBLEU-4: 0.126 -> 0.114Hurt
fine-tune unsupervised KGAE with 100% image-report pairsKGAE · MIMIC-CXRBLEU-4: 0.062 -> 0.118Helped
fine-tune with 60% pairs instead of unsupervised onlyKGAE · MIMIC-CXRclinical efficacy F1: 0.156 -> 0.307Helped

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