Abstract
Breast and thyroid lesions share many similarities in the feature representations of ultrasound images. However, there is a huge lesion scale gap between the two diseases, making it difficult to transfer knowledge between them through current methods for unsupervised domain adaptation. To address this problem, we propose a lesion scale matching approach where we employ a framework of latent space search for bounding box size to re-scale the source domain images, and then the Monte Carlo Expectation Maximization algorithm is used for optimization to match the lesion scales between the two disease domains. Extensive experimental results demonstrate the feasibility of cross-disease knowledge transfer, and our proposed method substantially improves the performance of unsupervised cross-disease domain adaptation models, with the Accuracy, Recall, Precision, and F1-score improved by 8.29%, 6.41%, 11.25%, and 9.14% on average in the three sets of ablation experiments.
J. Gao and Q. Lao—Equal contribution.
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Gao, J., Lao, Q., Kang, Q., Liu, P., Zhang, L., Li, K. (2022). Unsupervised Cross-disease Domain Adaptation by Lesion Scale Matching. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13437. Springer, Cham. https://doi.org/10.1007/978-3-031-16449-1_63
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