PerFact: Perception-Derived Fact Prompting for 3D Brain MRI Report Generation
Radiology report generation has matured almost entirely on 2D chest radiographs, where the default route to better reports is a larger backbone or a pre-training one on medical data. We revisit that assumption on 3D multi-sequence brain MRI, a volumetric multi-disease regime, and find that the model is not the lever. Zero-shot medical and radiology vision-language models transfer poorly to brain MRI, with chest radiograph specialists failing most conspicuously, and five backbones fine-tuned identically across three model families and an order of magnitude in scale differ only marginally. What
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- PossiblePossibly related (embedding) · 49%PnP-CoSMo: A Multi-Contrast MRI Reconstruction Framework based on Content/Style Modeling [R] →
- PossiblePossibly related (embedding) · 46%AI-backed imaging workflow helps generalist radiologists perform like breast specialists - Radiology Business →
- PossiblePossibly related (embedding) · 45%Smarter sonograms: Researcher uses generative AI to advance medical imaging - Medical Xpress →
- FuzzyOverlapping authors or contributors · 62%google-research/google-research →
“Shared author/contributor keys: sun”
- LinkedLinked via arxiv author · 85%Jianyu Sun →
“PerFact: Perception-Derived Fact Prompting for 3D Brain MRI Report Generation”
- LinkedLinked via arxiv author · 85%Zhenxuan Zhang →
“PerFact: Perception-Derived Fact Prompting for 3D Brain MRI Report Generation”
- LinkedLinked via arxiv author · 85%Guang Yang →
“PerFact: Perception-Derived Fact Prompting for 3D Brain MRI Report Generation”
- LinkedLinked via arxiv author · 85%Peter J. Lally →
“PerFact: Perception-Derived Fact Prompting for 3D Brain MRI Report Generation”