Multitask Classification Method Based on Label Correction for Breast Tumor Ultrasound Images

Abstract

To enable deep learning-based computer-aided diagnosis to achieve excellent performance in differentiating benign and malignant breast tumors in ultrasound images, a large number of labeled training samples must be collected. However, it is difficult to acquire sufficient samples due to the high costs of data collection and labeling. Fortunately, breast ultrasound images have two labels from different sources of domain knowledge: the biopsy results are “clean” labels, and the Breast Imaging Reporting and Data System (BI-RADS) score functions as a “noisy” label. Based on these two label types, we propose a multitask classification method based on label distribution correction (MTLC-Net). In our method, we propose different tasks to address the noisy and clean labels. Specifically, we propose a label distribution correction task for noisy labels that includes jointly training the network parameters and soft labels. The model is generalizable and robust by correcting the noisy label distribution based on the BI-RADS score, and it extracts knowledge from the noisy label task to improve the learning in the clean-label task. We conducted extensive comparisons with existing methods. Our method achieved a classification accuracy of 75.8%, a precision of 73.0%, a recall of 80.1% and an F1 score of 0.764—results that are significantly better than those of the existing state-of-the-art methods for differentiating benign and malignant breast tumors in ultrasound images.

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References

  1. 1.

    DeSantis C, Ma J, Sauer AG, Newman LA, Jemal A (2017) Breast cancer statistics, 2017, racial disparity in mortality by state. CA-Cancer J Clin 67(6):439–448

    Article  Google Scholar 

  2. 2.

    Singletary SE (2003) Rating the risk factors for breast cancer. Ann Surg 237(4):474–482

    Google Scholar 

  3. 3.

    Apantaku LM, Finch MD (2000) Breast cancer diagnosis and screening. Am Fam Physician 62(3):596–602

    Google Scholar 

  4. 4.

    Bhusri S, Jain S, Virmani J (2016) Classification of breast lesions based on laws’ feature extraction techniques. In: Proceedings of 2016 3rd international conference on computing for sustainable global development; New Delhi, India, 16–18 March 2016

  5. 5.

    Cheng HD, Shan J, Ju W, Guo Y, Zhang L (2010) Automated breast cancer detection and classification using ultrasound images: a survey. Pattern Recognit 43(1):299–317

    Article  Google Scholar 

  6. 6.

    Berg WA, Gutierrez L, NessAiver MS, Carter WB, Bhargavan M, Lewis RS, Ioffe OB (2004) Diagnostic accuracy of mammography, clinical examination, US, and MR imaging in preoperative assessment of breast cancer. Radiology 233(3):830–849

    Article  Google Scholar 

  7. 7.

    Berg WA, Blume JD, Cormack JB, Mendelson EB, Lehrer D, Böhm-Veléz M, Pisano ED, Jong RA, Evans WP, Morton MJ et al (2008) Combined screening with ultrasound and mammography vs mammography alone in women at elevated risk of breast cancer. JAMA J Am Med Assoc 299(18):2151–2163

    Article  Google Scholar 

  8. 8.

    Costantini M, Belli P, Lombardi R, Franceschini G, Mulè A, Bonomo L (2006) Characterization of solid breast masses: use of the sonographic breast imaging reporting and data system lexicon. J Ultrasound Med 25(5):649–659

    Article  Google Scholar 

  9. 9.

    Qi X, Zhang L, Chen Y, Pi Y, Chen Y, Lv Q, Yi Z (2019) Automated diagnosis of breast ultrasonography images using deep neural networks. Med Image Anal 52:185–198

    Article  Google Scholar 

  10. 10.

    Huynh B, Drukker K, Giger M (2016) MO-DE-207B-06: computer-aided diagnosis of breast ultrasound images using transfer learning from deep convolutional neural networks. Med Phys 43(6Part30):3705

    Article  Google Scholar 

  11. 11.

    Han S, Kang HK, Jeong JY, Park MH, Kim W, Bang WC, Seong YK (2017) A deep learning framework for supporting the classification of breast lesions in ultrasound images. Phys Med Biol 62(19):7714

    Article  Google Scholar 

  12. 12.

    Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention, Munich, Germany, 5–9 October 2015, pp 234–241

  13. 13.

    Cao Z, Yang G, Chen Q, Chen X, Lv F (2020) Breast tumor classification through learning from noisy labeled ultrasound images. Med Phys 47(3):1048–1057

    Article  Google Scholar 

  14. 14.

    Lee J (2017) Practical and illustrated summary of updated BI-RADS for ultrasonography. Ultrasonography 36(1):71–81

    Article  Google Scholar 

  15. 15.

    Shan J, Alam SK, Garra B, Zhang Y, Ahmed T (2016) Computer-aided diagnosis for breast ultrasound using computerized BI-RADS features and machine learning methods. Ultrasound Med Biol 42(4):980–988

    Article  Google Scholar 

  16. 16.

    Schlegl T, Ofner J, Langs G (2014) Unsupervised pre-training across image domains improves lung tissue classification. In: International MICCAI workshop on medical computer vision, Cambridge, MA, USA, 18 September 2014, pp 82–93

  17. 17.

    Vinyals O, Blundell C, Lillicrap T, Kavukcuoglu K, Wierstra D (2016) Matching networks for one shot learning. In: Advances in neural information processing systems, Barcelona, Spain, 5–10 December 2016, pp 3630–3638

  18. 18.

    Santoro A, Bartunov S, Botvinick M, Wierstra D, Lillicrap T (2016) Meta-learning with memory-augmented neural networks. In: International conference on machine learning, New York City, NY, USA, 19–24 June 2016, pp 1842–1850

  19. 19.

    Mishra N, Rohaninejad M, Chen X, Abbeel P (2018) A simple neural attentive meta-learner. In: International conference on learning representations, Vancouver, BC, Canada, 30 April–3 May 2018

  20. 20.

    Caruana R (1997) Multitask learning. Mach Learn 28(1):41–75

    MathSciNet  Article  Google Scholar 

  21. 21.

    Liu J, Li W, Zhao N, Cao K, Yin Y, Song Q, Chen H, Gong X (2018) Integrate domain knowledge in training CNN for ultrasonography breast cancer diagnosis. In: Proceedings of the medical image computing and computer assisted intervention (MICCAI 2018), Granada, Spain, 16–20 September 2018, pp 868–875

  22. 22.

    Shi J, Wu J, Lv P, Guo J (2019) BreastNet: entropy-regularized transferable multi-task learning for classification with limited breast data. Int J Biosci Biochem Bioinform 9(1):20–26

    Google Scholar 

  23. 23.

    Akselrod-Ballin A, Karlinsky L, Alpert S, Hasoul S, Ben-Ari R, Barkan E (2016) A region based convolutional network for tumor detection and classification in breast mammography. In: Proceedings of the international workshop on large-scale annotation of biomedical data and expert label synthesis, Athens, Greece, 21 October 2016, pp 197–205

  24. 24.

    Cao Z, Duan L, Yang G, Yue T, Chen Q, Fu H, Xu Y (2017) Breast tumor detection in ultrasound images using deep learning. In: Proceedings of the international workshop on patch-based techniques in medical imaging, Quebec City, Canada, 14 September 2017, pp 121–128

  25. 25.

    Han B, Yao Q, Yu X, Niu G, Xu M, Hu W, Tsang I, Sugiyama M (2018) Co-teaching: robust training of deep neural networks with extremely noisy labels. In: Proceedings of the annual conference on neural information processing systems 2018, Montréal, Canada, 3–8 December 2018, pp 8536–8546

  26. 26.

    He K, Sun J (2015) Convolutional neural networks at constrained time cost. In: Computer vision and pattern recognition, Boston, MA, USA, 7–12 June 2015, pp 5353–5360

  27. 27.

    Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: The 25th international conference on neural information processing systems, Lake Tahoe, Nevada, 3–6 December 2012, pp 1097–1105

  28. 28.

    Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: IEEE conference on computer vision and pattern recognition, Boston, MA, USA, 7–12 June, 2015, pp 1–9

  29. 29.

    He K, Zhang X, Ren S, Sun J (2016). Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA, 27–30 June, 2016, pp 770–778

  30. 30.

    Tanaka D, Ikami D, Yamasaki T, Aizawa K (2018) Joint optimization framework for learning with noisy labels. In: Proceedings of the 2018 IEEE conference on computer vision and pattern recognition, Salt Lake City, UT, USA, 18–22 June 2018, pp 5552–5560

  31. 31.

    Kun Y, Jianxin W (2019) Probabilistic end-to-end noise correction for learning with noisy labels. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR 2019), Long Beach, CA, USA, 16–20 June 2019, pp 7017–7025

  32. 32.

    Yanase J, Triantaphyllou E (2019) The seven key challenges for the future of computer-aided diagnosis in medicine. Int J Med Inf 129:413–422

    Article  Google Scholar 

Download references

Acknowledgements

This research was funded by the National Natural Science Foundation of China under Grant Numbers 61572109 and 61772006, the Science and Technology Program of Guangxi under Grant Number AB17129012, the Science and Technology Major Project of Guangxi under Grant Number AA17204096, the Special Fund for Scientific and Technological Bases and Talents of Guangxi under Grant Number 2016AD05050, and the Special Fund for Bagui Scholars of Guangxi.

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Correspondence to Xiaoyu Li or Qin Chen.

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Cao, Z., Yang, G., Li, X. et al. Multitask Classification Method Based on Label Correction for Breast Tumor Ultrasound Images. Neural Process Lett (2021). https://doi.org/10.1007/s11063-021-10455-4

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Keywords

  • Deep learning
  • Multitask learning
  • Breast ultrasound image
  • Noisy label