Reuss Joana, Pascual Guillem, Wenzek Hagen, Seguí Santi
Department of Mathematics and Computer Science, Universitat de Barcelona, 08007 Barcelona, Spain.
Chair of Remote Sensing Technology, Technical University of Munich, 80333 Munich, Germany.
Diagnostics (Basel). 2022 Feb 15;12(2):501. doi: 10.3390/diagnostics12020501.
Wireless Capsule Endoscopy (WCE) is a procedure to examine the human digestive system for potential mucosal polyps, tumours, or bleedings using an encapsulated camera. This work focuses on polyp detection within WCE videos through Machine Learning. When using Machine Learning in the medical field, scarce and unbalanced datasets often make it hard to receive a satisfying performance. We claim that using Sequential Models in order to take the temporal nature of the data into account improves the performance of previous approaches. Thus, we present a bidirectional Long Short-Term Memory Network (BLSTM), a sequential network that is particularly designed for temporal data. We find the BLSTM Network outperforms non-sequential architectures and other previous models, receiving a final Area under the Curve of 93.83%. Experiments show that our method of extracting spatial and temporal features yields better performance and could be a possible method to decrease the time needed by physicians to analyse the video material.
无线胶囊内镜检查（WCE）是一种使用封装摄像头检查人体消化系统是否存在潜在黏膜息肉、肿瘤或出血的程序。这项工作专注于通过机器学习在WCE视频中检测息肉。在医学领域使用机器学习时，稀缺且不平衡的数据集常常使得难以获得令人满意的性能。我们声称，使用顺序模型来考虑数据的时间特性可以提高先前方法的性能。因此，我们提出了一种双向长短期记忆网络（BLSTM），这是一种专门为时间数据设计的顺序网络。我们发现BLSTM网络优于非顺序架构和其他先前的模型，最终曲线下面积达到93.83%。实验表明，我们提取空间和时间特征的方法具有更好的性能，并且可能是减少医生分析视频材料所需时间的一种可行方法。
