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CoInNet:一个卷积-卷积网络,具有用于自动聚体细分的新型统计注意力

Samir Jain, Rohan Atale, Anubhav Gupta

IEEE transactions on medical imaging

|September 28, 2023

概括

CoInNet精确地细分微小的息肉,即使是图像区域的0.01%. 这种深度学习模型提高了胃肠道异常的早期诊断,优于现有的方法.

科学领域:

医疗成像医学成像
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

胃肠多是常见的,早期诊断有助于降低结肠直肠癌风险.
自动聚细分系统帮助外科医生,但由于聚变异性和不清楚的边界,他们面临着挑战.
现有的深度学习模型 (CNN,视觉转换器) 在捕捉各种视觉模式和特征依赖性方面存在局限性.

研究的目的:

提出 CoInNet,一种新的多胞体细分模型.
为了利用卷积和卷积运算进行增强的特征提取.
为了提高多片细分的准确性,特别是对于小而容易被忽视的多片.

主要方法:

开发了CoInNet,一种具有新特征提取机制的聚体细分模型.
集成的卷积和卷积操作以捕捉不同的视觉模式.
引入了一个统计特征注意力单元来学习特征地图之间的关系.
整合了一个异常边界近似模块,具有递归特征融合,以改进细分.

主要成果:

CoInNet精确地对聚体进行细分,包括占据图像面积仅为0.01%的微小聚体.
该模型在突出聚区域和学习准确的边界方面表现出卓越的性能.
在5个基准聚细分数据集上,CoInNet的表现优于13种最先进的方法.

结论:

CoInNet 在自动化多重体细分方面取得了重大进展.
该模型检测小息肉的能力对于临床应用至关重要,特别是在分析大型数据集时,如无线囊内镜视频.
CoInNet显示了改善早期诊断和降低结肠直肠癌风险的巨大潜力.

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