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MGCB前者:多级电网前和类间边界意识的变压器,用于聚片细分
Yang Xia1, Haijiao Yun1, Yanjun Liu1
1School of Electronic Information Engineering, Changchun University, Changchun, 130022, China.
Computers in biology and medicine
|November 6, 2023
概括
一个新的深度学习模型,MGCBFormer,改善了用于结直肠癌检测的聚细分. 这种先进的方法有效地利用数据,在准确性和概括性方面超过现有技术.
科学领域:
- 医学成像分析分析 医学成像分析
- 医疗保健中的人工智能
- 胃肠病学 胃肠病学
背景情况:
- 深度学习用于聚细分有助于早期检测结直肠癌.
- 目前的监督方法未充分利用像素级标签,过度关注骨干网络.
- 需要采用充分利用现有的多重体目标信息的方法.
研究的目的:
- 引入多级格子前和类间边界感知变压器 (MGCBFormer) 进行增强的多片细分.
- 为了解决主流监督多片细分技术的局限性.
- 提高肠道成像中多检测的效率和准确性.
主要方法:
- 开发MGCBFormer,结合一个多尺度的网格前和嵌套通道注意区块 (MGNAB).
- 整合一个类间边界意识区块 (CBB) 与边界预处理策略.
- 使用全局双轴联接器 (GDAC) 进行深度监控和噪声过.
主要成果:
- MGCBFormer在五个公开的息肉数据集 (Kvasir-SEG,CVC-ClinicDB,CVC-ColonDB,CVC-300,ETIS-LaribPolypDB) 中表现出卓越的预测性能.
- 对比实验表明,MGCBFormer的表现优于其他十二种聚细分方法.
- 该模型在聚合物细分任务中表现出强大的概括能力.
结论:
- MGCBFormer在基于深度学习的多片细分方面取得了重大进展.
- 拟议的架构有效地挖掘了多的目标信息和边界细节.
- 这项技术有望提高结直肠癌前体诊断的速度和准确性.
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