RL-ACRGNet applies reinforcement learning to an encoder-decoder architecture for radiology report generation and reports modest metric gains on IU-Xray and MIMIC-CXR.
Automated generation of accurate\& fluent medical x-ray reports,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
LLaMA-XR fine-tunes LLaMA 3.1 with QLoRA on DenseNet-121 embeddings to generate radiology reports from chest X-rays, reporting ROUGE-L of 0.433 and METEOR of 0.336 on the IU X-ray benchmark.
citing papers explorer
-
RL-ACRGNet: Reinforcement Learning-Based Chest Radiology Report Generation Network
RL-ACRGNet applies reinforcement learning to an encoder-decoder architecture for radiology report generation and reports modest metric gains on IU-Xray and MIMIC-CXR.
-
LLaMA-XR: A Novel Framework for Radiology Report Generation using LLaMA and QLoRA Fine Tuning
LLaMA-XR fine-tunes LLaMA 3.1 with QLoRA on DenseNet-121 embeddings to generate radiology reports from chest X-rays, reporting ROUGE-L of 0.433 and METEOR of 0.336 on the IU X-ray benchmark.