基于动态数据平衡策略的Xception双通道LSTM模型用于腹腔镜胆囊切除的阶段识别
Mingzhou Liu1, Feiya Duan1, Lin Ling2
1School of Mechanical Engineering, Hefei University of Technology, Hefei, Anhui, China.
International journal of computer assisted radiology and surgery
|September 7, 2025
概括
本研究引入了Xception双通道LSTM模型,具有动态数据平衡,以改善腹腔镜胆囊切除的阶段识别. 这种新的方法增强了时间特征的学习,并解决了类不平衡,提高了模型的整体性能.
科学领域:
- * 医学图像分析
- * 手术阶段识别
- *用于手术视频分析的深度学习
背景情况:
- * laparoscopic 胆囊切除术阶段识别对于手术安全性和训练至关重要.
- 现有的模型在手术数据集中的时间特征学习和类不平衡方面存在困难.
- 提升时间特征提取和数据平衡是提高模型准确性的关键.
研究的目的:
- * 开发一个先进的深度学习模型用于腹腔镜胆囊切除的阶段识别.
- * 提高时间特征学习能力和解决数据类不平衡.
- * 提出一个新的Xception双通道LSTM融合模型,并采用动态数据平衡策略.
主要方法:
- * 采用Xception模型以深度可分离的卷积来逐提取视觉特征.
- 使用双通道LSTM网络 (时间映射双向LSTM和序列嵌入LSTM) 来建模时间依赖.
- * 实施了动态数据平衡策略,以调整样本不足率并减轻偏差学习.
主要成果:
- * 拟议的Xception双通道LSTM模型在Cholec80数据集上表现优于传统的单通道LSTM模型.
- 与没有动态数据平衡的模型相比,所有手术阶段的F1分数都得到了改善.
- 实验结果证实了该模型在处理时间特征和阶级不平衡方面的有效性.
结论:
- * 动态数据平衡策略有效地缓解了手术阶段识别中的类不平衡问题.
- *Xception双通道LSTM融合模型在提取时间特征方面表现出卓越的性能.
- * 拟议的方法显著提高了腹腔镜胆囊切除阶段的整体检测性能.
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