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.
Highlights
Wideband tympanometry (WBT) provides a wealth of information on the middle ear mechanics in the form of a 3D diagram of the absorbance as a function of probe sound frequency and external ear pressure [1]
We focused on the few-shot WBT classification in otosclerosis and proposed a novel domain adaption algorithm named Gaussian processes-guided domain adaptation (GPGDA) to deal with the inevitable domain shift in deep transfer learning methods
The proposed algorithm employed the Gaussian processes to explicitly calibrate the distributions of the feature extractor’s outputs of the adopted backbone network between synthetic source dataset and realistic target dataset after pre-training, and the difference in distribution calibration was leveraged to optimize the parameters of the feature extractor with the help of gradient backpropagation algorithm
Summary
Wideband tympanometry (WBT) provides a wealth of information on the middle ear mechanics in the form of a 3D diagram of the absorbance as a function of probe sound frequency and external ear pressure [1]. 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 further optimize its parameters on the small target dataset that was concerned. 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. The selection of the hyperparameters in the proposed algorithm was assessed via numerical experiments
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