渐进性疾病图像生成与普通意识的扩散模型
Meryem Mine Kurt1,2, Ümit Mert Çağlar2, Alptekin Temizel2
1ASELSAN Inc., Ankara 06200, Turkey.
Diagnostics (Basel, Switzerland)
|October 29, 2025
概括
本研究介绍了人工智能模型的顺序嵌入,以生成现实的性结肠炎 (UC) 进展图像,改进疾病建模和计算机辅助诊断. 新方法有效地捕捉疾病的严重程度,以便更好地生成合成数据.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 胃肠病学 胃肠病学
背景情况:
- 性结肠炎 (UC) 缺乏纵向视觉数据,阻碍了疾病进展的建模.
- 目前的计算机辅助诊断系统受到稀疏的中期疾病阶段和像梅奥内镜评分 (MES) 这样的离散评分系统的限制.
研究的目的:
- 为条件扩散模型开发新的顺序嵌入架构,以从内镜图像生成现实的UC进展序列.
- 解决稀疏的中期疾病阶段和UC的离散评分的局限性.
主要方法:
- 适应稳定扩散 v1.4 具有专门的顺序嵌入 (基本顺序嵌入和附加顺序嵌入).
- 将离散的MES类别转换为使用顺序嵌入的连续进度表示.
- 建模累积病理特征以增强疾病表征.
主要成果:
- 与替代品相比,添加式顺序嵌入器显示出更好的分布对齐和疾病一致性.
- 生成的序列显示了严重程度之间的平稳过渡,并保持了解剖学真实性.
- 取得了强的绩效指标,包括CMMD 0.4137,召回 0.6331,方程加权卡帕 0.8425和UMAP轮得分 0.0571.
结论:
- 建立了将静态医疗数据集转换为UC的动态进展模型的基础.
- 证明了顺序意识嵌入在捕捉疾病严重程度关系中的有效性.
- 实现了代表性不足的中期疾病阶段的合成,支持医学教育,诊断和合成数据生成中的应用.
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