μ^2Tokenizer: Differentiable Multi-Scale Multi-Modal Tokenizer for
  Radiology Report Generation
Authors: name: Siyou Li; name: Pengyao Qin; name: Huanan Wu; name: Dong Nie; name: Arun J. Thirunavukarasu; name: Juntao Yu; name: Le Zhang
Published: 2025-06-30 23:14:49

Abstract: Automated radiology report generation (RRG) aims to produce detailed textual
reports from clinical imaging, such as computed tomography (CT) scans, to
improve the accuracy and efficiency of diagnosis and provision of management
advice. RRG is complicated by two key challenges: (1) inherent complexity in
extracting relevant information from imaging data under resource constraints,
and (2) difficulty in objectively evaluating discrepancies between
model-generated and expert-written reports. To address these challenges, we
propose mu^2LLM, a textbf{mu}ltiscale
textbf{mu}ltimodal large language models for RRG tasks. The
novel {mu}^2Tokenizer, as an intermediate layer, integrates multi-modal
features from the multiscale visual tokenizer and the text tokenizer, then
enhances report generation quality through direct preference optimization
(DPO), guided by GREEN-RedLlama. Experimental results on four large CT
image-report medical datasetdemonstrate that our method outperforms existing
approaches, highlighting the potential of our fine-tuned mu^2LLMs on limited
data for RRG tasks.