XFM:一种可解释的知识融合视觉基础模型,用于改善低流行视网膜疾病的临床诊断
IEEE journal of biomedical and health informatics
|November 24, 2025
概括
眼科基础模型可以在低数据环境中检测罕见的视网膜疾病. 预先知识输入和SAM引导的规范化提高了模型性能和可解释性,以获得可靠的眼科护理人工智能.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 与专业模型相比,基础模型提供了更高的概括性.
- 在临床环境中适应这些模型用于低患病率的视网膜疾病是具有挑战性的.
研究的目的:
- 研究眼科基础模型的适应性,以检测低患病率的视网膜疾病.
- 通过先验知识输入和SAM引导的规范化来提高模型的可解释性.
主要方法:
- 转移学习和基础模型的微调.
- 整合细粒度的先验知识输入.
- 应用SAM引导循环约束规范化的应用,以提供可解释性.
主要成果:
- 在FundusData-FS和OTFID数据集上进行有效的转移学习和微调性能.
- 在性能和可解释性方面都取得了明显的改进.
- 在特定测试中,与RETFound相比,获得了超过9%的精度改善和8%的AUC增长.
结论:
- 可解释的眼科基础模型显示出临床应用的巨大潜力.
- 预先知识输入和规范化提高了眼科人工智能的可靠性.
- 开发的方法对于检测低发病率视网膜疾病是有效的.
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