Public articles linked to the same research event.
arXiv The work introduces Fact-Flow, a framework that decouples visual fact identification from report generation: an LLM automatically extracts and merges clinical finding labels from training reports (7 labels for tuberculosis, 42 for ophthalmology), a multi-label classifier predicts findings, and the predicted labels are serialized into a prompt to guide an MLLM; on the tuberculosis chest X-ray dataset MedGemma + Fact-Flow reaches RadFact F1 0.3055 versus 0.2266 for MedGemma alone, and on the ophthalmology dataset Qwen2.5-VL + Fact-Flow is best on most NLG metrics.
The work introduces Fact-Flow, a framework that decouples visual fact identification from report generation: an LLM automatically extracts and merges clinical finding labels from training reports (7 labels for tuberculosis, 42 for ophthalmology), a multi-label classifier predicts findings, and the predicted labels are serialized into a prompt to guide an MLLM; on the tuberculosis chest X-ray dataset MedGemma + Fact-Flow reaches RadFact F1 0.3055 versus 0.2266 for MedGemma alone, and on the ophthalmology dataset Qwen2.5-VL + Fact-Flow is best on most NLG metrics.
The work introduces Fact-Flow, a framework that decouples visual fact identification from report generation: an LLM automatically extracts and merges clinical finding labels from training reports (7 labels for tuberculosis, 42 for ophthalmology), a multi-label classifier predicts findings, and the predicted labels are serialized into a prompt to guide an MLLM; on the tuberculosis chest X-ray dataset MedGemma + Fact-Flow reaches RadFact F1 0.3055 versus 0.2266 for MedGemma alone, and on the ophthalmology dataset Qwen2.5-VL + Fact-Flow is best on most NLG metrics.
The work introduces Fact-Flow, a framework that decouples visual fact identification from report generation: an LLM automatically extracts and merges clinical finding labels from training reports (7 labels for tuberculosis, 42 for ophthalmology), a multi-label classifier predicts findings, and the predicted labels are serialized into a prompt to guide an MLLM; on the tuberculosis chest X-ray dataset MedGemma + Fact-Flow reaches RadFact F1 0.3055 versus 0.2266 for MedGemma alone, and on the ophthalmology dataset Qwen2.5-VL + Fact-Flow is best on most NLG metrics.