AnchorDiff is a topology-aware masked diffusion framework with RadGraph anchors and confidence-based rewriting that claims state-of-the-art results on MIMIC-CXR and MIMIC-RG4 for radiology report generation.
L.; Bannur, S.; Bouzid, K.; Castro, D
8 Pith papers cite this work. Polarity classification is still indexing.
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MARL-Rad trains region-specific and global agents with reinforcement learning on clinical rewards to produce more accurate radiology reports than prior methods on MIMIC-CXR and IU X-ray datasets.
RA-RRG extracts key phrases with LLMs, retrieves them via multimodal similarity, and conditions report generation on them to achieve SOTA CheXbert scores and competitive RadGraph F1 on MIMIC-CXR and IU X-ray while supporting multi-view inputs.
Med-Gemini sets new records on 10 of 14 medical benchmarks including 91.1% on MedQA-USMLE, beats GPT-4V by 44.5% on multimodal tasks, and surpasses humans on medical text summarization.
CXRMate-2 improves chest X-ray report generation via temporal embeddings and tractable RL, delivering metric gains and 45% acceptability in radiologist review with no significant preference difference on most findings.
Lingshu is a medical-specialized multimodal LLM that outperforms prior open-source models on multimodal QA, text QA, and report generation after training on a large curated dataset of medical knowledge.
M4CXR is a multi-modal large language model that performs multiple tasks in chest X-ray analysis including report generation with claimed SOTA clinical accuracy using chain-of-thought prompting.
MedGemma 1.5 4B reports absolute gains of 11% on 3D MRI classification, 3% on 3D CT, 47% macro F1 on pathology slides, 35% IoU on anatomical localization, and 5-22% on clinical QA tasks over MedGemma 1.
citing papers explorer
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AnchorDiff: Topology-Aware Masked Diffusion with Confidence-based Rewriting for Radiology Report Generation
AnchorDiff is a topology-aware masked diffusion framework with RadGraph anchors and confidence-based rewriting that claims state-of-the-art results on MIMIC-CXR and MIMIC-RG4 for radiology report generation.
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Multi-Modal Multi-Agent Reinforcement Learning for Radiology Report Generation
MARL-Rad trains region-specific and global agents with reinforcement learning on clinical rewards to produce more accurate radiology reports than prior methods on MIMIC-CXR and IU X-ray datasets.
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RA-RRG: Multimodal Retrieval-Augmented Radiology Report Generation with Key Phrase Extraction
RA-RRG extracts key phrases with LLMs, retrieves them via multimodal similarity, and conditions report generation on them to achieve SOTA CheXbert scores and competitive RadGraph F1 on MIMIC-CXR and IU X-ray while supporting multi-view inputs.
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Capabilities of Gemini Models in Medicine
Med-Gemini sets new records on 10 of 14 medical benchmarks including 91.1% on MedQA-USMLE, beats GPT-4V by 44.5% on multimodal tasks, and surpasses humans on medical text summarization.
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CXRMate-2: Structured Multimodal Temporal Embeddings and Tractable Reinforcement Learning for Clinically Acceptable Chest X-ray Radiology Report Generation
CXRMate-2 improves chest X-ray report generation via temporal embeddings and tractable RL, delivering metric gains and 45% acceptability in radiologist review with no significant preference difference on most findings.
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Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning
Lingshu is a medical-specialized multimodal LLM that outperforms prior open-source models on multimodal QA, text QA, and report generation after training on a large curated dataset of medical knowledge.
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M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretation
M4CXR is a multi-modal large language model that performs multiple tasks in chest X-ray analysis including report generation with claimed SOTA clinical accuracy using chain-of-thought prompting.
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MedGemma 1.5 Technical Report
MedGemma 1.5 4B reports absolute gains of 11% on 3D MRI classification, 3% on 3D CT, 47% macro F1 on pathology slides, 35% IoU on anatomical localization, and 5-22% on clinical QA tasks over MedGemma 1.