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MOTC:腹部多目标细分模型与全球和本地信息的并行融合
GuoDong Zhang1, WenWen Gu1, SuRan Wang1
1School of Computer, Shenyang Aerospace University, Daoyi South Street, Shenyang, 110135, Liaoning Province, China.
Journal of imaging informatics in medicine
|February 12, 2024
概括
一个新的并行混合模型通过结合卷积神经网络 (CNN) 和变压器来增强医疗图像细分. 这种方法有效地解决了局部诱导偏差,提高了对具有挑战性的数据集的细分精度.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 卷积神经网络 (CNN) 在医疗图像细分中很普遍,但由于局部诱导偏差,它们与远程依赖性作斗争.
- 变压器擅长模拟远程依赖关系,在细分和分类任务中提供改进.
- 现有的混合CNN-变压器模型在充分利用变压器潜力方面存在局限性,原因是偏差消除的细粒度.
研究的目的:
- 为医疗图像细分提出一种新的并行混合模型,克服现有方法的局限性.
- 调查偏差消除细度对CNN-变压器架构中局部偏差消除有效性的影响.
- 为了提高医疗图像细分的准确性和性能.
主要方法:
- 一个平行混合模型,在编码器中具有独立的变压器和CNN分支,用于特征提取.
- 层间信息融合和分支之间的交换,用于同时进行本地和全球特征提取.
- 一个纯粹的卷积解码器,用于生成最终的细分结果.
- 在Flare21和Amos22数据集上进行的实验,以评估模型性能.
主要成果:
- 拟议的并行混合模型在Flare21数据集上实现了92.65%的平均Dice系数,在Amos22数据集上达到91.61%.
- 该模型超过了医学图像细分中的比较方法的性能.
- 实验结果表明,偏差消除操作中的较小颗粒度会导致更高的性能.
结论:
- 平行混合模型有效地提取了本地和全球特征,同时减轻了本地偏差.
- 偏差消除操作中的较小颗粒度对于提高CNN-Transformer模型在医疗图像细分中的性能至关重要.
- 拟议的方法显示了提高医疗图像细分精度的巨大潜力.
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