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SelectiveKD: A Semi-supervised Framework for Cancer Detection in DBT Through Knowledge Distillation and Pseudo-labeling

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Cancer Prevention, Detection, and Intervention (CaPTion 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15199))

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Abstract

When developing Computer Aided Detection (CAD) systems for Digital Breast Tomosynthesis (DBT), the complexity arising from the volumetric nature of the modality poses significant technical challenges for obtaining large-scale accurate annotations. Without access to large-scale annotations, the resulting model may not generalize to different domains. Given the costly nature of obtaining DBT annotations, how to effectively increase the amount of data used for training DBT CAD systems remains an open challenge.

In this paper, we present SelectiveKD, a semi-supervised learning framework for building cancer detection models for DBT, which only requires a limited number of annotated slices to reach high performance. We achieve this by utilizing unlabeled slices available in a DBT stack through a knowledge distillation framework in which the teacher model provides a supervisory signal to the student model for all slices in the DBT volume. Our framework mitigates the potential noise in the supervisory signal from a sub-optimal teacher by implementing a selective dataset expansion strategy using pseudo labels.

We evaluate our approach with a large-scale real-world dataset of over 10,000 DBT exams collected from multiple device manufacturers and locations. The resulting SelectiveKD process effectively utilizes unannotated slices from a DBT stack, leading to significantly improved cancer classification performance (AUC) and generalization performance.

L. Dillard, H. Lee and W. Lee–These authors contributed equally to this work.

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References

  1. Andharia, D., Shah, H., Prajapati, A.D., Bhansali, A.D., Shah, A., Desai, D.: Digital breast tomosynthesis(DBT) vs 2D mammography and impact of combined use: a meta-analysis. In: medRxiv (2023)

    Google Scholar 

  2. Bahl, M., Gaffney, S., McCarthy, A.M., Lowry, K.P., Dang, P.A., Lehman, C.D.: Breast cancer characteristics associated with 2d digital mammography versus digital breast tomosynthesis for screening-detected and interval cancers. Radiology 287(1), 49–57 (2018)

    Article  Google Scholar 

  3. Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., Raffel, C.A.: MixMatch: a holistic approach to semi-supervised learning. Adv. Neural Inf. Process. Syst. 32 (2019)

    Google Scholar 

  4. Chen, Y., Mancini, M., Zhu, X., Akata, Z.: Semi-supervised and unsupervised deep visual learning: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 46, 1327–1347 (2022)

    Article  Google Scholar 

  5. Chen, Y., Zhu, X., Li, W., Gong, S.: Semi-supervised learning under class distribution mismatch. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 3569–3576 (2020)

    Google Scholar 

  6. Chikarmane, S.A., Cochon, L.R., Khorasani, R., Sahu, S., Giess, C.S.: Screening mammography performance metrics of 2D digital mammography versus digital breast tomosynthesis in women with a personal history of breast cancer. Am. J. Roentgenol. 217, 587–594 (2021)

    Article  Google Scholar 

  7. DeLong, E.R., DeLong, D.M., Clarke-Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44, 837–845 (1988)

    Article  Google Scholar 

  8. Dhamija, E., Gulati, M., Deo, S., Gogia, A., Hari, S.: Digital breast tomosynthesis: an overview. Indian J. Surg. Oncol. 12(2), 315–329 (2021)

    Article  Google Scholar 

  9. Goldberg, J.E., et al.: New horizons: artificial intelligence for digital breast tomosynthesis. Radiographics 43(1), e220060 (2022)

    Article  Google Scholar 

  10. Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network (2015)

    Google Scholar 

  11. Huang, L., Chen, Y., He, X.: Spectral-spatial masked transformer with supervised and contrastive learning for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 61, 1–18 (2023)

    Google Scholar 

  12. Ko, M.J., et al.: Accuracy of digital breast tomosynthesis for detecting breast cancer in the diagnostic setting: a systematic review and meta-analysis. Korean J. Radiol. 22(8), 1240 (2021)

    Article  Google Scholar 

  13. Lee, D.H., et al.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on Challenges in Representation Learning, ICML, Atlanta, vol. 3, p. 896 (2013)

    Google Scholar 

  14. Lee, W., Lee, H., Lee, H., Park, E.K., Nam, H., Kooi, T.: Transformer-based deep neural network for breast cancer classification on digital breast tomosynthesis images. Radiol. Artif. Intell. 5(3), e220159 (2023)

    Article  Google Scholar 

  15. Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)

  16. Nguyen, T., et al.: Overview of digital breast tomosynthesis: clinical cases, benefits, and disadvantages. Diagn. Interv. Imaging 96(9), 843–859 (2015)

    Article  Google Scholar 

  17. Shahan, C.L.: An overview of digital breast tomosynthesis. W. Va. Med. J. 2016, 996 (2016)

    Google Scholar 

  18. Shen, Y., Shi, L., Zhao, J., Dong, Y., Wang, L.: Fully convolutional spectral-spatial fusion network integrating supervised contrastive learning for hyperspectral image classification. IEEE J. Sel. Topics Appl. Earth Obs. Rem. Sens. 16, 9077–9088 (2023)

    Article  Google Scholar 

  19. Shoshan, Y., et al.: Artificial intelligence for reducing workload in breast cancer screening with digital breast tomosynthesis. Radiology 303(1), 69–77 (2022)

    Article  Google Scholar 

  20. Singh, P., et al.: Shifting to machine supervision: annotation-efficient semi and self-supervised learning for automatic medical image segmentation and classification. arXiv preprint arXiv:2311.10319 (2023)

  21. Tamé, I.d.A., Sirotkin, K., Carballeira, P., Escudero-Viñolo, M.: Self-supervised curricular deep learning for chest X-ray image classification. arXiv preprint arXiv:2301.10687 (2023)

  22. Tardy, M., Mateus, D.: Trainable summarization to improve breast tomosynthesis classification. In: de Bruijne, M. (ed.) MICCAI 2021. LNCS, vol. 12907, pp. 140–149. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87234-2_14

    Chapter  Google Scholar 

  23. Xie, Q., Luong, M.T., Hovy, E., Le, Q.V.: Self-training with noisy student improves imageNet classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10687–10698 (2020)

    Google Scholar 

  24. Zhang, J., Yang, J., Yu, J., Fan, J.: Semisupervised image classification by mutual learning of multiple self-supervised models. Int. J. Intell. Syst. 37(5), 3117–3141 (2022)

    Article  Google Scholar 

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Correspondence to Ali Diba .

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Dillard, L., Lee, H., Lee, W., Kim, T.S., Diba, A., Kooi, T. (2025). SelectiveKD: A Semi-supervised Framework for Cancer Detection in DBT Through Knowledge Distillation and Pseudo-labeling. In: Ali, S., van der Sommen, F., Papież, B.W., Ghatwary, N., Jin, Y., Kolenbrander, I. (eds) Cancer Prevention, Detection, and Intervention. CaPTion 2024. Lecture Notes in Computer Science, vol 15199. Springer, Cham. https://doi.org/10.1007/978-3-031-73376-5_15

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  • DOI: https://doi.org/10.1007/978-3-031-73376-5_15

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