基于编码器和解码器的模型对CT报告中的可操作发现进行比较评估.
Yusuke Fukui1, Naoki Harada2, Yukihiro Nagatani2
1Department of Radiology, Shiga University of Medical Science Hospital, Seta Tsukinowa-Cho, Otsu, Shiga, 520-2192, Japan. fyusuke@belle.shiga-med.ac.jp.
Journal of imaging informatics in medicine
|March 10, 2026
概括
我们比较了编码器和解码器模型,用于自动化可操作的放射学报告通知. 编码器微调优于足够的数据,而解码器提示则为高度不平衡的数据集提供了切实可行的替代方案.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
背景情况:
- 针对可操作的计算机断层扫描 (CT) 报告的通知工作流程的自动化对于及时的临床干预至关重要.
- 在医疗报告中常见的高度不平衡的数据集,对机器学习模型的性能构成重大挑战.
研究的目的:
- 调查和比较编码器和解码器模型在不同程度的数据不平衡下在头部CT报告中检测可操作发现的性能.
- 引导选择适当的AI模型用于对放射学报告进行分类,并提供可操作的发现.
主要方法:
- 使用1000个头部CT报告的数据集,将一个监督微调 (SFT) 编码器模型 (ModernBERT-ja) 与一个通过几次拍摄提示,零拍摄CoT和SFT评估的解码器模型 (Llama-3-ELYZA-JP-8B) 进行比较.
- 训练数据中的阳性率被系统地降低,以评估模型的稳定性 (14.4%降至2%).
- 使用F1评分来评估性能,特别注意编码器的类加权焦点损失和解码器的演示订单.
主要成果:
- 带有类加权焦点损失的SFT编码器在14.4%的正比率下获得了F1得分0.870,超过了解码器最好的几次拍摄方法 (F1=0.717).
- 编码器性能显著降低到5%的阳性率以下,而解码器则以少数拍摄提示显示改进,对演示顺序敏感.
- SFT提高了解码器的F1分数,达到0.853,但增加了培训成本;两种模型都没有保持2%的正比率.
结论:
- 当有足够的阳性病例可用时,编码器SFT是可操作的发现检测的最佳选择.
- 解码器少量射击提示为高度不平衡的数据集提供了可行的,低培训的替代方案,在这些数据集中,少数群体的采样具有挑战性.
- 模型的选择取决于数据的可用性和放射学报告选的具体要求,以获得可行的发现.
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