Purpose

An endocytoscope is a new type of endoscope that enables users to perform conventional endoscopic observation and ultramagnified observation at the cell level. Although endocytoscopy is expected to improve the cost-effectiveness of colonoscopy, endocytoscopic image diagnosis requires much knowledge and high-level experience for physicians. To circumvent this difficulty, we developed a robust endocytoscopic (EC) image classification method for the construction of a computer-aided diagnosis (CAD) system, since real-time CAD can resolve accuracy issues and reduce interobserver variability.

Method

We propose a novel feature extraction method by introducing higher-order symmetric tensor analysis to the computation of multi-scale topological statistics on an image, and we integrate this feature extraction with EC image classification. We experimentally evaluate the classification accuracy of our proposed method by comparing it with three deep learning methods. We conducted this comparison by using our large-scale multi-hospital dataset of about 55,000 images of over 3800 patients.

Results

Our proposed method achieved an average 90% classification accuracy for all the images in four hospitals even though the best deep learning method achieved 95% classification accuracy for images in only one hospital. In the case with a rejection option, the proposed method achieved expert-level accurate classification. These results demonstrate the robustness of our proposed method against pit pattern variations, including differences of colours, contrasts, shapes, and hospitals.

Conclusions

We developed a robust EC image classification method with novel feature extraction. This method is useful for the construction of a practical CAD system, since it has sufficient generalisation ability.

中文翻译：

---

基于高阶对称张量分析和多尺度拓扑统计的鲁棒内窥镜图像分类。

目的

内窥镜是一种新型的内窥镜，使用户能够在细胞水平上执行常规内窥镜观察和超放大观察。尽管内窥镜检查有望提高结肠镜检查的成本效益，但内窥镜检查图像诊断需要医生具备丰富的知识和丰富的经验。为了解决这一难题，我们开发了一种强大的内窥镜（EC）图像分类方法来构建计算机辅助诊断（CAD）系统，因为实时CAD可以解决准确性问题并减少观察者之间的差异。

方法

通过将高阶对称张量分析引入图像的多尺度拓扑统计中，我们提出了一种新颖的特征提取方法，并将该特征提取与EC图像分类相结合。通过与三种深度学习方法进行比较，我们通过实验评估了该方法的分类准确性。我们使用我们的大型多医院数据集进行了比较，该数据集包含3800多例患者的约55,000张图像。

结果

我们提出的方法对四家医院的所有图像均实现了90％的平均分类精度，即使最好的深度学习方法仅对一家医院的图像实现了95％的分类精度。在具有拒绝选项的情况下，所提出的方法实现了专家级的准确分类。这些结果证明了我们提出的方法对凹坑图案变化（包括颜色，对比度，形状和医院差异）的鲁棒性。

结论

我们开发了一种具有新颖特征提取的鲁棒EC图像分类方法。该方法具有足够的泛化能力，因此对于构建实用的CAD系统很有用。