使用DeepLabV3+的结肠镜图像中的多体细分
Al Mohimanul Islam1, Sadia Shakiba Bhuiyan1, Mysun Mashira1
1Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh.
Computers in biology and medicine
|August 28, 2025
概括
这项研究引入了一种增强的DeepLabv3++模型,用于精确分离结肠镜图像. 改进的模型显著减少了细分错误,有助于早期发现结直肠癌.
科学领域:
- 医学成像
- 人工智能
- 计算机视觉
背景情况:
- 结肠直肠癌是全球癌症死亡的主要原因.
- 在结肠镜检查中精确分片对于早期诊断至关重要.
- 现有的深度学习模型在小细节和多尺度特征表示方面存在困难.
研究的目的:
- 开发一个增强的DeepLabv3++模型,以改善结肠镜图像中的息肉细分.
- 为了提高聚检测的精度和稳定性.
- 为了更好的临床决策,减少细分错误.
主要方法:
- 在编码器中使用EfficientNetV2S进行精细的特征提取.
- 综合多层次金字塔聚合 (MSPP) 和并行注意力聚合区 (PAAB) 模块.
- 实现了重新设计的解码器,用于增强特征转换和细分地图生成.
主要成果:
- 达到了高的子系数: 96. 20% (CVC-ColonDB), 96. 54% (CVC-ClinicDB) 和 96. 08% (Kvasir-SEG).
- 在聚细分方面表现优于几种最先进的模型.
- 与基线DeepLabv3+相比,所有大小的息肉的细分错误 (错误阳性/阴性) 显著减少.
结论:
- 增强的DeepLabv3++模型在结肠镜聚细分方面表现出卓越的性能.
- 整合MSPP和重新设计的解码器提高了模型捕获多尺度和定向特征的能力.
- 这一进步对于精确的结核划分和结直肠癌查的临床决策至关重要.
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