Pseudo-labeled bootstrapping and multi-stage transfer learning for the classification and localization of dysplasia in Barrett’s esophagus

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 esphageal cancer, this work concentrates on improving the state of the art for the computer-aided classification and localization of dysplastic lesions in BE. To this end, we employ a large-scale endoscopic data set, consisting of 494, 355 images, to pre-train several instances of the proposed GastroNet architecture, after which several data sets that are increasingly closer to the target domain are used in a multi-stage transfer learning strategy. Finally, ensembling is used to evaluate the results on a prospectively gathered external test set. 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% while preserving sensitivity at a high level, thereby reducing the false positive rate substantially. Our algorithm also significantly outperforms state-of-the-art on the localization metrics, where the intersection of all experts is correctly indicated in approximately 92% of the cases.