Few-Shot Wideband Tympanometry Classification in Otosclerosis via Domain Adaptation with Gaussian Processes

Abstract

Otosclerosis is a common middle ear disease that requires a combination of examinations for its diagnosis in routine. In a previous study, we showed that this disease could be potentially diagnosed by wideband tympanometry (WBT) coupled with a convolutional neural network (CNN) in a rapid and non-invasive manner. We showed that deep transfer learning with data augmentation could be applied successfully on such a task. However, the involved synthetic and realistic data have a significant discrepancy that impedes the performance of transfer learning. To address this issue, a Gaussian processes-guided domain adaptation (GPGDA) algorithm was developed. It leveraged both the loss about the distribution distance calculated by the Gaussian processes and the loss of conventional cross entropy during the transferring. On a WBT dataset including 80 otosclerosis and 55 control samples, it achieved an area-under-the-curve of 97.9±1.1 percent after receiver operating characteristic analysis and an F1-score of 95.7±0.9 percent that were superior to the baseline methods (r=10, p<0.05, ANOVA). To understand the algorithm’s behavior, the role of each component in the GPGDA was experimentally explored on the dataset. In conclusion, our GPGDA algorithm appears to be an effective tool to enhance CNN-based WBT classification in otosclerosis using just a limited number of realistic data samples.