Stable polyp-scene classification via subsampling and residual learning from an imbalanced large dataset
Stable polyp-scene classification via subsampling and residual learning from an imbalanced large dataset
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DOI：
10.1049/htl.2019.0079
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发表时间：
2019-11
影响因子：
2.1
通讯作者：
H. Itoh;H. Roth;M. Oda;M. Misawa;Y. Mori;S. Kudo;K. Mori
中科院分区：
文献类型：
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作者：
H. Itoh;H. Roth;M. Oda;M. Misawa;Y. Mori;S. Kudo;K. Mori
这封信提出了一种稳定的息肉场景分类方法，具有低假阳性（FP）检测。结肠镜检查期间精确的自动息肉检测对于预防结肠癌死亡至关重要。因此，需要一种用于结肠镜检查的计算机辅助诊断（CAD）系统来辅助结肠镜检查医师。通过三维卷积神经网络（3D CNN）进行时空特征提取的高性能CAD系统在实际结肠镜视频中的检测准确率约为80%。因此，进一步改进具有更大训练数据的3D CNN是可行的。然而，息肉和非息肉场景之间的比例在大的结肠镜视频数据集中是相当不平衡的。这种不平衡导致不稳定的息肉检测。为了解决这个问题，作者提出了一种用于深度残差学习的高效且平衡的学习技术。作者的方法随机选择非息肉场景的子集，其数量与每个学习时期开始时息肉场景的静止图像的数量相同。此外，他们还引入了稳定的多场景分类的后处理。这种后处理减少了在多场景分类的实际应用中出现的FP。他们使用由1027个结肠镜视频组成的大型息肉检测数据集评估了几个残差网络。在场景级评估中，他们提出的方法实现了稳定的多场景分类，灵敏度为0.86，特异性为0.97。
This Letter presents a stable polyp-scene classification method with low false positive (FP) detection. Precise automated polyp detection during colonoscopies is essential for preventing colon-cancer deaths. There is, therefore, a demand for a computer-assisted diagnosis (CAD) system for colonoscopies to assist colonoscopists. A high-performance CAD system with spatiotemporal feature extraction via a three-dimensional convolutional neural network (3D CNN) with a limited dataset achieved about 80% detection accuracy in actual colonoscopic videos. Consequently, further improvement of a 3D CNN with larger training data is feasible. However, the ratio between polyp and non-polyp scenes is quite imbalanced in a large colonoscopic video dataset. This imbalance leads to unstable polyp detection. To circumvent this, the authors propose an efficient and balanced learning technique for deep residual learning. The authors’ method randomly selects a subset of non-polyp scenes whose number is the same number of still images of polyp scenes at the beginning of each epoch of learning. Furthermore, they introduce post-processing for stable polyp-scene classification. This post-processing reduces the FPs that occur in the practical application of polyp-scene classification. They evaluate several residual networks with a large polyp-detection dataset consisting of 1027 colonoscopic videos. In the scene-level evaluation, their proposed method achieves stable polyp-scene classification with 0.86 sensitivity and 0.97 specificity.