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一个反复的多式联动稀疏变压器框架用于胃肠道疾病的分类
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, India.
Scientific reports
|July 7, 2025
概括
这项研究引入了一个新的AI框架,用于使用文本和图像诊断胃肠道疾病. 循环多式主梯度K-近端稀疏变压器 (RMP-GKPS-变压器) 在分类胃肠道疾病方面实现了高精度.
科学领域:
- 胃肠病学和人工智能的人工智能
- 医学图像分析 医学图像分析
- 医学中的自然语言处理.
背景情况:
- 准确的胃肠道 (GI) 疾病诊断至关重要,但由于数据不一致而受到挑战.
- 现有的方法与模式失衡作斗争,并且在文本和内镜图像中具有冗余性.
- 对于GI疾病分类的异质数据整合需要先进的多式联接融合策略.
研究的目的:
- 开发一种新型的反复多式联通主梯度K-近端稀疏变压器 (RMP-GKPS-变压器) 框架.
- 通过使用集成的临床文本和无线囊内镜 (WCE) 图像,提高肠胃疾病分类的准确性和可解释性.
- 解决现有诊断框架在处理多式联运数据方面的局限性.
主要方法:
- 集成的临床文本和WCE图像使用Bio-RoBERTa用于文本特征和图形视觉空间通道注意力转换器用于图像特征.
- 采用交叉注意力机制来实现模式对齐,主要组件分析 (PCA) 来减少维度,以及梯度增强机器 (GBM) 来解决语义冲突.
- 使用集成分类器,包括随机森林KNN,近距离政策优化 (PPO) 和稀疏辐射基函数 (RBF) 内核.
主要成果:
- 在公开可用的数据集上实现了99.82%的准确性和98.7%的子系数.
- 与最先进的方法相比,显著减少了执行时间.
- 成功调整并利用多式联络数据来精确分类六种胃肠道疾病.
结论:
- RMP-GKPS-变压器框架为GI疾病分类提供了一个可扩展和可解释的解决方案.
- 该研究强调了多式联络数据融合在改善胃肠病学临床决策方面的潜力.
- 提出的方法有效地克服了诊断数据中的模式失衡和特征冗余.
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