放大内镜下乳头内毛细血管袢分类：开放数据集和基线方法学
Intrapapillary Capillary Loop Classification in Magnification Endoscopy: Open Dataset and Baseline Methodology
摘要：
目的.食管早期鳞状细胞瘤（ESCN）是一种高度可治疗的疾病。局限于粘膜层的病变可通过内窥镜治疗。我们建立了一个计算机辅助检测（CADe）系统，可以将静止图像或视频帧分类为正常或异常，诊断准确率高。方法.我们提出了一个新的基准数据集，其中包含从114个患者视频中提取的68K二进制标记帧，其成像区域已被切除并与组织病理学相关。我们的新型卷积网络（CNN）架构解决了二进制分类任务，并解释了输入域的哪些特征驱动了网络的决策过程。结果所提出的方法实现了91.7%的平均准确率相比，由12名高级临床医生组成的一组达到了94.7%。我们的新型网络架构产生了深度监督的激活热图，表明网络在预测异常时正在观察乳头内毛细血管环（IPCL）模式。结论我们相信，这个数据集和基线方法可以作为未来ESCN检测背景下视频帧分类和可解释性基准的参考。一个具有高度临床相关性的未来工作路径是将分类扩展到ESCN类型。
英文摘要：
Purpose. Early squamous cell neoplasia (ESCN) in the oesophagus is a highly treatable condition. Lesions confined to the mucosal layer can be curatively treated endoscopically. We build a computer-assisted detection (CADe) system that can classify still images or video frames as normal or abnormal with high diagnostic accuracy. Methods. We present a new benchmark dataset containing 68K binary labeled frames extracted from 114 patient videos whose imaged areas have been resected and correlated to histopathology. Our novel convolutional network (CNN) architecture solves the binary classification task and explains what features of the input domain drive the decision-making process of the network. Results. The proposed method achieved an average accuracy of 91.7 % compared to the 94.7 % achieved by a group of 12 senior clinicians. Our novel network architecture produces deeply supervised activation heatmaps that suggest the network is looking at intrapapillary capillary loop (IPCL) patterns when predicting abnormality. Conclusion. We believe that this dataset and baseline method may serve as a reference for future benchmarks on both video frame classification and explainability in the context of ESCN detection. A future work path of high clinical relevance is the extension of the classification to ESCN types.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
