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HTC-Net: Hashimoto’s thyroiditis ultrasound image classification model based on residual network reinforced by channel attention mechanism

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Abstract

Convolutional neural network (CNN) is efficient in extracting and aggregating local features in the spatial dimension of the images. However, obtaining the inapparent texture information of the low-echo area in the ultrasound images is not easy, and it is especially challenging for the early lesion recognition in Hashimoto’s thyroiditis (HT) ultrasound images. In this paper, a HT ultrasound image classification model HTC-Net based on residual network reinforced by channel attention mechanism is proposed. HTC-Net strengthens the features of the important channels by reinforced channel attention mechanism through which the high-level semantic information is enchanced and the low-level semantic information is suppressed. Residual network assists HTC-Net focus on the key local areas of the ultrasound images while pay attention to the global semantic information. Furthermore, in order to solve the problem of uneven distribution caused by large amount of difficult-to-classify samples in the data sets, a new feature loss function TanCELoss with weight factor dynamically adjusting is constructed. TanCELoss function can better assist HTC-Net to transform difficult-to-classify samples into easy-to-classify samples gradually, and improve the balancing distribution of the samples. The experiments are implemented based on data sets collected by the Endocrinology Department of four branches from Guangdong Provincial Hospital of Chinese Medicine. Both quantitative testing and visualization results show that HTC-Net obtains STOA performance for early lesions recognition in HT ultrasound images. HTC-Net has great application value especially under the condition of owning only small data samples.

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Data availability

The use of HT ultrasound images for related research has been approved by the Medical Ethical Committee Approval of Guangdong Provincial Hospital of Chinese Medicine (Number: ZE-2021- 264-01, Date: 07 Sep. 2021).

References

  1. Liontiris MI, Mazokopakis EE. A concise review of Hashimoto thyroiditis (HT) and the importance of iodine, selenium, vitamin D and gluten on the autoimmunity and dietary management of HT patients. Points that need more investigation. Hell J Nucl Med. 2017;20:51–6.

    Google Scholar 

  2. Ragusa F, et al. Hashimotos’ thyroiditis: epidemiology, pathogenesis, clinic and therapy. Best Pract Res Clin Endocrinol Metab. 2019;33: 101367.

    Article  Google Scholar 

  3. American Thyroid Association. Hashimoto’s Thyroiditis Brochure. American Thyroid Association; 2019. https://www.thyroid.org/wp-content/uploads/patients/brochures/Hashimoto_Thyroiditis.pdf.

  4. Chen Q, Min X, Duan H, Zhu Y, Zhai G. Muiqa: image quality assessment database and algorithm for medical ultrasound images. In: 2021 IEEE international conference on image processing (ICIP), Anchorage, AK, USA; 2021. p. 2958–2962. https://doi.org/10.1109/ICIP42928.2021.9506431.

  5. Qi Y, Guo Y, Wang Y. Image quality enhancement using a deep neural network for plane wave medical ultrasound imaging. IEEE Trans Ultrason Ferroelectr Freq Control. 2021;68(4):926–34. https://doi.org/10.1109/TUFFC.2020.3023154.

    Article  Google Scholar 

  6. Li X, et al. Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study. Lancet Oncol. 2019;20:193–201.

    Article  Google Scholar 

  7. Pilikos G, Horchens L, Van Leeuwen T, Lucka F. Deep learning for multi-view ultrasonic image fusion. In: 2021 IEEE international ultrasonics symposium (IUS), Xi’an, China; 2021. p. 1–4. https://doi.org/10.1109/IUS52206.2021.9593507.

  8. Ohnishi T, et al. Image feature conversion of pathological image for registration with ultrasonic image. In: 2018 International workshop on advanced image technology (IWAIT), Chiang Mai, Thailand; 2018. p. 1–2. https://doi.org/10.1109/IWAIT.2018.8369800.

  9. Wang Q, Liu D, Liu G. Value of ultrasonic image features in diagnosis of perinatal outcomes of severe preeclampsia on account of deep learning algorithm. Comput Math Methods Med. 2022;2022:4010339:1-4010339:10. https://doi.org/10.1155/2022/4010339.

    Article  Google Scholar 

  10. Wei K, Wang B, Saniie J. Faster region convolutional neural networks applied to ultrasonic images for breast lesion detection and classification. In: 2020 IEEE international conference on electro information technology (EIT); 2020. p.p. 171–4. https://doi.org/10.1109/EIT48999.2020.9208264.

  11. Zhao X, Gong X, Fan L, Luo J. Attention-based networks of human breast bimodal ultrasound imaging classification. J Image Graph. 2022;27(3):0911–22. https://doi.org/10.11834/jig.210370.

    Article  Google Scholar 

  12. He M, Zhang R, Liu S, Tan Y, Zeng Y. Ultrasound image diagnosis of liver and spleen injury based on a double-channel convolutional neural network. Wirel Commun Mob Comput. 2021;2021:32827011:2-3827011:9. https://doi.org/10.1155/2021/2827011.

    Article  Google Scholar 

  13. Zeimarani B, Costa MGF, Nurani NZ, Bianco SR, De Albuquerque Pereira WC, Filho CFFC. Breast lesion classification in ultrasound images using deep convolutional neural network. IEEE Access. 2020;8:133349–59. https://doi.org/10.1109/ACCESS.2020.3010863.

    Article  Google Scholar 

  14. Wang Y, Choi EJ, Choi Y, Zhang H, Jin GY, Ko SB. Breast cancer classification in automated breast ultrasound using multiview convolutional neural network with transfer learning. Ultrasound Med Biol. 2020;46(5):1119–32. https://doi.org/10.1016/j.ultrasmedbio.2020.01.001.

    Article  Google Scholar 

  15. Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q. ECA-Net: efficient channel attention for deep convolutional neural networks. 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR); 2020. p. 11531–9. https://doi.org/10.1109/CVPR42600.2020.01155.

  16. Hu J, Shen L, Albanie S, Sun G, Wu E. Squeeze-and-excitation networks. IEEE Trans Pattern Anal Mach Intell. 2020;42(8):2011–23. https://doi.org/10.1109/TPAMI.2019.2913372.

    Article  Google Scholar 

  17. Acharya UR, et al. Diagnosis of Hashimoto’s thyroiditis in ultrasound using tissue characterization and pixel classification. Proc Inst Mech Eng H. 2013;227:788–98.

    Article  Google Scholar 

  18. Ma L, et al. Thyroid diagnosis from SPECT images using convolutional neural network with optimization. Comput Intell Neurosci. 2019. https://doi.org/10.1155/2019/6212759.

    Article  Google Scholar 

  19. Zhao W, et al. Convolutional neural network-based computer-assisted diagnosis of Hashimoto’s thyroiditis on ultrasound. J Clin Endocrinol Metab. 2022;107:953–63.

    Article  Google Scholar 

  20. Zhang Q, et al. Deep learning to diagnose Hashimoto’s thyroiditis from sonographic images. Nat Commun. 2022. https://doi.org/10.1038/s41467-022-31449-3.

    Article  Google Scholar 

  21. Song CH, Han HJ, Avrithis Y. All the attention you need: Global-local, spatial-channel attention for image retrieval. In: 2022 IEEE/CVF winter conference on applications of computer vision (WACV); 2022. p. 439–48. https://doi.org/10.1109/WACV51458.2022.00051.

  22. 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); 2016. p. 770–8. https://doi.org/10.1109/CVPR.2016.90.

  23. Lin T-Y, Goyal P, Girshick R, He K, Dollár P. Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell. 2020;42(2):318–27. https://doi.org/10.1109/TPAMI.2018.2858826.

    Article  Google Scholar 

  24. Singh BK, Verma K, Thoke AS. Adaptive gradient descent backpropagation for classification of breast tumors in ultrasound imaging. In: Proc. Int. Conf. Inf. Commun. Technol. (ICICT), vol. 46; 2015. p. 1601–9.

  25. Ribani R, Marengoni M. A survey of transfer learning for convolutional neural networks. In: 2019 32nd SIBGRAPI conference on graphics, patterns and images tutorials (SIBGRAPI-T); 2019. p. 47–57. https://doi.org/10.1109/SIBGRAPI-T.2019.00010.

  26. Li Y, Song PH. Review of transfer learning in medical image classification. J Image Graph. 2022;27(3):0672–86. https://doi.org/10.11834/jig.210814.

    Article  Google Scholar 

  27. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017;60:84–90. https://doi.org/10.1145/3065386.

    Article  Google Scholar 

  28. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE international conference on computer vision (ICCV), Venice, Italy; 2017. p. 618–626. : https://doi.org/10.1109/ICCV.2017.74.

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Acknowledgements

EU H2020 CLARIFY (CLoud ARtificial Intelligence For pathologY). Grant No. 860627. H2020-MSCA-ITN-2019 call.

Funding

This work was supported by National Natural Science Foundation of China (Grant No. 62072202).

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Authors and Affiliations

  1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, China

    Zhipeng Liang, Kang Chen, Tianchun Luo & Wenchao Jiang

  2. Department of Endocrinology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, 510120, China

    Jianxuan Wen, Ling Zhao & Wei Song

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  1. Zhipeng Liang
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Correspondence to Wenchao Jiang or Wei Song.

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Liang, Z., Chen, K., Luo, T. et al. HTC-Net: Hashimoto’s thyroiditis ultrasound image classification model based on residual network reinforced by channel attention mechanism. Health Inf Sci Syst 11, 24 (2023). https://doi.org/10.1007/s13755-023-00225-y

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