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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 3

Medical imaging report generation via multi-modal large language models with discrimination-enhanced fine-tuning

Qian Qian1,2
Sun Liping1
Liu Jialin1,2
Du Huijiang1
Ling Chen1
1. Medical Instrumentation College of Shanghai University of Medicine & Health Sciences, Shanghai 201318, China
2. School of Health Sciences & Engineering, University of Shanghai for Science & Technology, Shanghai 200093, China
DOI: 10.19734/j.issn.1001-3695.2024.08.0303

Abstract

Objective: Automated medical imaging report generation significantly enhances the work efficiency of radiologists. Most traditional methods for generating medical imaging reports rely on classification or image caption models, which exhibit deficiencies in accuracy, fluency, and diversity. Fine-tuning techniques with large language models are a promising way to address these issues. Methods: This paper proposes a discrimination-enhanced fine-tuning method called MedVLM, based on a pre-trained multi-modal large language model. Classification labels for specific diseases serve as auxiliary targets in fine-tuning. Fine-tuning techniques such as low-rank adaptation, P-Tuning V2, and Freeze refine the feature extraction, vision-language conversion, and large language model modules. These approaches enable accurate diagnosis of diseases in lung CT images and facilitates the generation of higher-quality reports. Results: MedVLM achieves a BLEU@4 score of 40.85% (range 40.41%–40.94%) , a METEOR score of 70.56% (range 70.37%–70.8%) , and a pneumonia diagnosis accuracy rate of 87.67% (range 86.06%–87.39%) , significantly surpassing traditional image caption methods. Conclusion: The discriminate-enhanced fine-tuning method for large pre-trained multi-modal language models significantly improves the accuracy of lung CT image report generation and demonstrates broad application potential.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.08.0303
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 3

Publish History

[2024-12-11] Accepted Paper

Cite This Article

钱乾, 孙丽萍, 刘佳霖, 等. 基于判别增强大语言模型微调的医学影像报告生成 [J]. 计算机应用研究, 2025, 42 (3). (2024-12-16). https://doi.org/10.19734/j.issn.1001-3695.2024.08.0303. (Qian Qian, Sun Liping, Liu Jialin, et al. Medical imaging report generation via multi-modal large language models with discrimination-enhanced fine-tuning [J]. Application Research of Computers, 2025, 42 (3). (2024-12-16). https://doi.org/10.19734/j.issn.1001-3695.2024.08.0303. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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