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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sung, H., et al.: Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Can. J. Clin. 71(3), 209–249 (2021)
Agarwal, D.P., Soni, T.P., Sharma, O.P., Sharma, S.: Synchronous malignancies of breast and thyroid gland: a case report and review of literature. J. Can. Res. Ther. 3(3), 172–173 (2007)
Chen, J., et al.: Correlation analysis of breast and thyroid nodules: a cross-sectional study. Int. J. Gener. Med. 14, 3999–4010 (2021)
An, J.H., et al.: A possible association between thyroid cancer and breast cancer. Thyroid 25(12), 1330–1338 (2015). PMID: 26442580
Yi-Cheng, Z., et al.: A generic deep learning framework to classify thyroid and breast lesions in ultrasound images. Ultrasonics 110, 106300 (2021)
Sahiner, B., et al.: Malignant and benign breast masses on 3d us volumetric images: effect of computer-aided diagnosis on radiologist accuracy. Radiology 242(3), 716–724 (2007). PMID: 17244717
Chen, K., et al.: Enhanced breast lesion classification via knowledge guided cross-modal and semantic data augmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 53–63. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87240-3_6
Liu, T., et al.: Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks. Med. Image Anal. 58, 101555 (2019)
Qian, X., et al.: Prospective assessment of breast cancer risk from multimodal multiview ultrasound images via clinically applicable deep learning. Nat. Biomed. Eng. 5, 1–11 (2021)
Sharifi, Y., Bakhshali, M.A., Dehghani, T., DanaiAshgzari, M., Sargolzaei, M., Eslami, S.: Deep learning on ultrasound images of thyroid nodules. Biocybern. Biomed. Eng. 41(2), 636–655 (2021)
Nguyen, D.T., Kang, J.K., Pham, T.D., Batchuluun, G., Park, K.R.: Ultrasound image-based diagnosis of malignant thyroid nodule using artificial intelligence. Sensors 20(7), 1822 (2020)
Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2030–2096 (2016)
Long, M., Cao, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.), Advances in Neural Information Processing Systems, vol. 31. Curran Associates Inc (2018)
Zhang, Y., Liu, T., Long, M., Jordan, M.: Bridging theory and algorithm for domain adaptation. In: Proceedings of the 36th International Conference on Machine Learning, pp. 7404–7413. PMLR (2019)
Yang, Y., Soatto, S.: Fda: fourier domain adaptation for semantic segmentation. In: CVPR, pp. 4084–4094 (2020)
Lao, Q., Jiang, X., Havaei, M.: Hypothesis disparity regularized mutual information maximization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 8243–8251 (2021)
Touvron, H., Vedaldi, A., Douze, M., Jegou, H.: Fixing the train-test resolution discrepancy. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’ Alché-Buc, F., Fox, E., Garnett, R. (eds.), Advances in Neural Information Processing Systems, vol. 32. Curran Associates Inc (2019)
McLachlan, G.J., Krishnan, T.: The EM Algorithm and Extensions. Wiley Series in Probability and Statistics. Wiley (2007)
Jiang, Y.X., Liu, H., Liu, J.B., Zhu, Q.L., Sun, Q., Chang, X.Y.: Breast tumor size assessment: comparison of conventional ultrasound and contrast-enhanced ultrasound. Ultrasound Med. Biol. 33, 1873–1881 (2007)
Golshan, M., Fung, B.B., Wiley, E., Wolfman, J., Rademaker, A., Morrow, M.: Prediction of breast cancer size by ultrasound, mammography and core biopsy. Breast 13(4), 265–271 (2004)
Shoma, A., Moutamed, A., Ameen, M., Abdelwahab, A.: Ultrasound for accurate measurement of invasive breast cancer tumor size. Breast J. 12(3), 252–256 (2006)
Zheng, X., et al.: Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer. Nat. Commun. 11, 03 (2020)
Cavallo, A., et al.: Thyroid nodule size at ultrasound as a predictor of malignancy and final pathologic size. Thyroid 27, 01 (2017)
Al-Dhabyani, W., Gomaa, M., Khaled, H., Fahmy, A.: Dataset of breast ultrasound images. Data Brief 28, 104863 (2020)
Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 32 (2019)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778. IEEE Computer Society, Los Alamitos, CA, USA, June 2016
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y
Woong-Gi, C., You, T., Seo, S., Kwak, S., Han, B.: Domain-specific batch normalization for unsupervised domain adaptation. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-16449-1_63
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16448-4
Online ISBN: 978-3-031-16449-1
eBook Packages: Computer ScienceComputer Science (R0)
