Yang Jie, Ou Yan, Chen Zhiqian, Liao Juan, Sun Wenjian, Luo Yang, Luo Chunbo
IEEE J Biomed Health Inform. 2023 Jan;27(1):7-16. doi: 10.1109/JBHI.2022.3217944. Epub 2023 Jan 4.
Endoscopy has been routinely used to diagnose stomach diseases including intestinal metaplasia (IM) and gastritis atrophy (GA). Such routine examination usually demands highly skilled radiologists to focus on a single patient with substantial time, causing the following two key challenges: 1) the dependency on the radiologist's experience leading to inconsistent diagnosis results across different radiologists; 2) limited examination efficiency due to the demanding time and energy consumption to the radiologist. This paper proposes to address these two issues in endoscopy using novel machine learning method in three-folds. Firstly, we build a novel and relatively big endoscopy dataset of 21,420 images from the widely used White Light Imaging (WLI) endoscopy and more recent Linked Color Imaging (LCI) endoscopy, which were annotated by experienced radiologists and validated with biopsy results, presenting a benchmark dataset. Secondly, we propose a novel machine learning model inspired by the human visual system, named as local attention grouping, to effectively extract key visual features, which is further improved by learning from multiple randomly selected regional images via ensemble learning. Such a method avoids the significant problem in the deep learning methods that decrease the resolution of original images to reduce the size of input samples, which would remove smaller lesions in endoscopy images. Finally, we propose a dual transfer learning strategy to train the model with co-distributed features between WLI and LCI images to further improve the performance. The experiment results, measured by accuracy, specificity, sensitivity, positive detection rate and negative detection rate, on IM are 99.18 %, 98.90 %, 99.45 %, 99.45 %, 98.91 %, respectively, and on GA are 97.12 %, 95.34 %, 98.90 %, 98.86 %, 95.50 %, respectively, achieving state of the art performance that outperforms current mainstream deep learning models.
内窥镜检查已被常规用于诊断包括肠化生（IM）和胃炎萎缩（GA）在内的胃部疾病。这种常规检查通常需要高技能的放射科医生花费大量时间专注于单个患者，从而带来以下两个关键挑战：1）依赖放射科医生的经验导致不同放射科医生的诊断结果不一致；2）由于对放射科医生要求的时间和精力消耗，检查效率有限。本文提出通过新颖的机器学习方法分三个方面解决内窥镜检查中的这两个问题。首先，我们从广泛使用的白光成像（WLI）内窥镜检查和更新的联动彩色成像（LCI）内窥镜检查中构建了一个包含21420张图像的新颖且相对较大的内窥镜数据集，这些图像由经验丰富的放射科医生标注并经活检结果验证，形成了一个基准数据集。其次，我们提出了一种受人类视觉系统启发的新颖机器学习模型，称为局部注意力分组，以有效提取关键视觉特征，并通过集成学习从多个随机选择的区域图像中学习进一步改进。这种方法避免了深度学习方法中为减少输入样本大小而降低原始图像分辨率的重大问题，而这会去除内窥镜图像中的较小病变。最后，我们提出一种双重迁移学习策略，用WLI和LCI图像之间的共同分布特征训练模型，以进一步提高性能。在IM上，以准确率、特异性、灵敏度、阳性检测率和阴性检测率衡量的实验结果分别为99.18%、98.90%、99.45%、99.45%、98.91%，在GA上分别为97.12%、95.34%、98.90%、98.86%、95.50%，实现了优于当前主流深度学习模型的最优性能。
