TY - GEN
T1 - Pseudo-labeled Bootstrapping and Multi-stage Transfer Learning for the Classification and Localization of Dysplasia in Barrett’s Esophagus
AU - van der Putten, Joost
AU - de Groof, Jeroen
AU - van der Sommen, Fons
AU - Struyvenberg, Maarten
AU - Zinger, Svitlana
AU - Curvers, Wouter
AU - Schoon, Erik
AU - Bergman, Jacques
AU - de With, Peter H. N.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85075665566&origin=inward
U2 - https://doi.org/10.1007/978-3-030-32692-0_20
DO - https://doi.org/10.1007/978-3-030-32692-0_20
M3 - Conference contribution
SN - 9783030326913
VL - 11861 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 169
EP - 177
BT - Machine Learning in Medical Imaging - 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Proceedings
A2 - Suk, Heung-Il
A2 - Liu, Mingxia
A2 - Yan, Pingkun
PB - Springer
T2 - 10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 13 October 2019
ER -