在医学成像中重新思考U-Net架构:推进用于结直肠多片细分的高效和可解释的UKAN-CBAM框架
Md Faysal Ahamed1, Fariya Bintay Shafi1, Md Rabiul Islam2
1Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh.
Artificial intelligence in medicine
|January 20, 2026
概括
这项研究介绍了UKAN-CBAM,这是一种用于检测结直肠聚的新型AI框架,显著提高了癌症预防医学成像的准确性和效率. 该模型展示了强大的概括和实时功能,用于临床使用.
科学领域:
- 医学成像和人工智能 医学成像和人工智能
- 计算病理学计算病理学
- 在瘤学瘤学.
背景情况:
- 及时检测结直肠多瘤对于预防结直肠癌至关重要.
- 手动的息肉检测受到高成本,专业知识要求和易受错误的挑战.
- 现有的方法往往缺乏可解释性和效率.
研究的目的:
- 开发一个先进的语义细分框架,UKAN-CBAM,用于增强结直肠多体检测.
- 将科尔莫戈罗夫-阿诺德网络 (KAN) 与卷积块注意模块 (CBAM) 集成,以提高性能.
- 为了实现临床应用的计算效率高和可解释的模型.
主要方法:
- 拟议的UKAN-CBAM框架将KAN和CBAM结合在一个U-Net架构中.
- 关于Kvasir-SEG数据集的培训和多个外部数据集的验证.
- 使用十倍交叉验证和Grad-CAM进行稳定性和可解释性分析.
主要成果:
- 英国CBAM实现了卓越的性能,以93.80%的mDice和89.18%的mIoU超过了SOTA方法.
- 证明了计算效率,使用55.99 MB的内存和122.272 ms的推断速度.
- 通过对 t 试验和交叉验证证实统计显著性,突出显示模型的稳定性.
结论:
- UKAN-CBAM提供了一种有效和可靠的工具,用于实时临床应用在结直肠多检测.
- 注意力机制和可解释性的整合标志着医学诊断的重大进步.
- 该框架在各种数据集中显示出强大的概括能力.
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