# Few-Shot Wideband Tympanometry Classification in
###### 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 \(97.9\pm 1.1\) percent after receiver operating characteristic analysis and an F1-score of \(95.7\pm 0.9\) 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.  
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In a previous study, we showed that, with the help of data augmentation and transfer learning, CNN could achieve an accurate classification of otosclerosis versus healthy ears using only a small amount of annotated WBT data [5]. This method would pre-train the network on a similar but different large _source dataset_ and then further optimize its parameters on the small _target dataset_ that was actually concerned. The source dataset employed for pre-training could be a large open-source dataset for the general classification task such as ImageNet [16], or it could also be generated with the help of prior knowledge [17] or data augmentation [18]. However, there is an inevitable bias between the source and target datasets due to the inherent nature of the transfer learning solution. Hence, it is essential to address the issue of _domain shift_ for effective transferring, and the _domain adaptation_ method could usually be adopted to deal with this hurdle [19].  
In this article, a deep transfer learning solution was adopted to perform WBT classification for automatic diagnosis of otosclerosis under the constraint of limited labeled WBT data. The source dataset was synthesized by data augmentation. We focused on the issue of domain shift between the synthetic source and realistic target datasets and proposed a novel approach of domain adaptation to leverage the source dataset applied to the transfer learning and to improve the diagnostic accuracy. Our approach measured the difference between the source and target datasets via Gaussian processes (GP) and incorporated the information into the parameter optimisation loop when training on the small but focused target dataset in order to guide the transferring. The performance of our algorithm was experimentally verified on an open-source WBT dataset for the otosclerosis diagnosis task [5] and was compared with the state-of-the-art methods. Additionally, the selection of the hyperparameters in the proposed algorithm was assessed via numerical experiments.