Patients suffering from Barrett's Esophagus (BE) are at an increased risk of developing esophageal adenocarcinoma and early detection is crucial for a good prognosis. To aid the endoscopists with the early detection for this preliminary stage of esophageal cancer, this work concentrates on the development and extensive evaluation of a state-of-the-art computer-aided classification and localization algorithm for dysplastic lesions in BE. To this end, we have employed a large-scale endoscopic data set, consisting of 494,355 images, in combination with a novel semi-supervised learning algorithm to pretrain several instances of the proposed neural network architecture. Next, several Barrett-specific data sets that are increasingly closer to the target domain with significantly more data compared to other related work, were used in a multi-stage transfer learning strategy. Additionally, the algorithm was evaluated on two prospectively gathered external test sets and compared against 53 medical professionals. Finally, the model was also evaluated in a live setting without interfering with the current biopsy protocol. Results from the performed experiments show that the proposed model improves on the state-of-the-art on all measured metrics. More specifically, compared to the best performing state-of-the-art model, the specificity is improved by more than 20% points while simultaneously preserving high sensitivity and reducing the false positive rate substantially. Our algorithm yields similar scores on the localization metrics, where the intersection of all experts is correctly indicated in approximately 92% of the cases. Furthermore, the live pilot study shows great performance in a clinical setting with a patient level accuracy, sensitivity, and specificity of 90%. Finally, the proposed algorithm outperforms each individual medical expert by at least 5% and the average assessor by more than 10% over all assessor groups with respect to accuracy.

患有巴雷特食管 (BE) 的患者患食管腺癌的风险增加，早期发现对于良好的预后至关重要。为了帮助内窥镜医师及早发现食管癌的这个初步阶段，这项工作集中于对 BE 中发育不良病变的最先进计算机辅助分类和定位算法的开发和广泛评估。为此，我们采用了一个由 494,355 张图像组成的大规模内窥镜数据集，并结合了一种新颖的半监督学习算法来预训练所提出的神经网络架构的几个实例。接下来，几个 Barrett 特定的数据集越来越接近目标域，与其他相关工作相比，数据明显更多，用于多阶段迁移学习策略。此外，该算法在两个前瞻性收集的外部测试集上进行了评估，并与 53 名医疗专业人员进行了比较。最后，还在现场环境中对模型进行了评估，而不会干扰当前的活检方案。执行实验的结果表明，所提出的模型改进了所有测量指标的最新技术。更具体地说，与性能最佳的最先进模型相比，特异性提高了 20% 以上，同时保持高灵敏度并大幅降低假阳性率。我们的算法在定位指标上产生了相似的分数，其中所有专家的交集在大约 92% 的情况下被正确指示。此外，实时试点研究在临床环境中显示出出色的性能，患者水平的准确度、灵敏度和特异性为 90%。最后，在准确性方面，所提出的算法在所有评估人员组中的表现优于每个单独的医学专家至少 5% 和平均评估人员超过 10%。