DiagR1, a Qwen2.5-VL model post-trained with SFT and GRPO and prompted with classifier outputs, substantially improves BLEU and BERTScore for GI pathology report generation, though clinical-relevance claims lack supporting evaluation.
Patterns of upper gas- trointestinal diseases among patients undergoing esophagogastroduo- denoscopy at three hospitals in asella town, southeast ethiopia,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
eess.IV 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
support 1representative citing papers
citing papers explorer
-
DiagR1: A Vision-Language Model Trained via Reinforcement Learning for Digestive Pathology Diagnosis
DiagR1, a Qwen2.5-VL model post-trained with SFT and GRPO and prompted with classifier outputs, substantially improves BLEU and BERTScore for GI pathology report generation, though clinical-relevance claims lack supporting evaluation.