Random Direct Preference Optimization for Radiography Report Generation
Fuente:
arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914057046458368 |
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| author | Samokhin, Valentin Shirokikh, Boris Goncharov, Mikhail Umerenkov, Dmitriy Bobrin, Maksim Oseledets, Ivan Dylov, Dmitry Belyaev, Mikhail |
| author_facet | Samokhin, Valentin Shirokikh, Boris Goncharov, Mikhail Umerenkov, Dmitriy Bobrin, Maksim Oseledets, Ivan Dylov, Dmitry Belyaev, Mikhail |
| contents | Radiography Report Generation (RRG) has gained significant attention in medical image analysis as a promising tool for alleviating the growing workload of radiologists. However, despite numerous advancements, existing methods have yet to achieve the quality required for deployment in real-world clinical settings. Meanwhile, large Visual Language Models (VLMs) have demonstrated remarkable progress in the general domain by adopting training strategies originally designed for Large Language Models (LLMs), such as alignment techniques. In this paper, we introduce a model-agnostic framework to enhance RRG accuracy using Direct Preference Optimization (DPO). Our approach leverages random contrastive sampling to construct training pairs, eliminating the need for reward models or human preference annotations. Experiments on supplementing three state-of-the-art models with our Random DPO show that our method improves clinical performance metrics by up to 5%, without requiring any additional training data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21351 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Random Direct Preference Optimization for Radiography Report Generation Samokhin, Valentin Shirokikh, Boris Goncharov, Mikhail Umerenkov, Dmitriy Bobrin, Maksim Oseledets, Ivan Dylov, Dmitry Belyaev, Mikhail Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Radiography Report Generation (RRG) has gained significant attention in medical image analysis as a promising tool for alleviating the growing workload of radiologists. However, despite numerous advancements, existing methods have yet to achieve the quality required for deployment in real-world clinical settings. Meanwhile, large Visual Language Models (VLMs) have demonstrated remarkable progress in the general domain by adopting training strategies originally designed for Large Language Models (LLMs), such as alignment techniques. In this paper, we introduce a model-agnostic framework to enhance RRG accuracy using Direct Preference Optimization (DPO). Our approach leverages random contrastive sampling to construct training pairs, eliminating the need for reward models or human preference annotations. Experiments on supplementing three state-of-the-art models with our Random DPO show that our method improves clinical performance metrics by up to 5%, without requiring any additional training data. |
| title | Random Direct Preference Optimization for Radiography Report Generation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2509.21351 |