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Deep Learning Hyperparameter Optimization for Breast Mass Detection in Mammograms

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Advances in Visual Computing (ISVC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13599))

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Abstract

Accurate breast cancer diagnosis through mammography has the potential to save millions of lives around the world. Deep learning (DL) methods have shown to be very effective for mass detection in mammograms Additional improvements of current DL models will further improve the effectiveness of these methods. A critical issue in this context is how to pick the right hyperparameters for DL models. In this paper, we present GA-E2E, a new approach for tuning the hyperparameters of DL models for breast cancer detection using Genetic Algorithms (GAs). Our findings reveal that differences in parameter values can considerably alter the area under the curve (AUC), which is used to determine a classifier’s performance.

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References

  1. Aboutalib, S.S., Mohamed, A.A., Berg, W.A., Zuley, M.L., Sumkin, J.H., Wu, S.: Deep learning to distinguish recalled but benign mammography images in breast cancer screeningdeep learning in mammography. Clin. Cancer Res. 24(23), 5902–5909 (2018)

    Article  Google Scholar 

  2. Arevalo, J., González, F.A., Ramos-Pollán, R., Oliveira, J.L., Lopez, M.A.G.: Convolutional neural networks for mammography mass lesion classification. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 797–800. IEEE (2015)

    Google Scholar 

  3. Bäck, T., Fogel, D.B., Michalewicz, Z.: Evolutionary Computation 1: Basic Algorithms and Operators. CRC Press, Boca Raton (2018)

    Google Scholar 

  4. Benitez-Hidalgo, A., Nebro, A.J., Garcia-Nieto, J., Oregi, I., Del Ser, J.: jmetalpy: a python framework for multi-objective optimization with metaheuristics. Swarm Evol. Comput. 51, 100598 (2019)

    Article  Google Scholar 

  5. Bulat, A., Tzimiropoulos, G.: How far are we from solving the 2D & 3D face alignment problem?(and a dataset of 230,000 3D facial landmarks). In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1021–1030 (2017)

    Google Scholar 

  6. Ciresan, D., Giusti, A., Gambardella, L., Schmidhuber, J.: Deep neural networks segment neuronal membranes in electron microscopy images. In: 25th Proceedings of the Conference on Advances in Neural Information Processing Systems (2012)

    Google Scholar 

  7. Cole, E.B., Zhang, Z., Marques, H.S., Hendrick, R.E., Yaffe, M.J., Pisano, E.D.: Impact of computer-aided detection systems on radiologist accuracy with digital mammography. AJR Am. J. Roentgenol. 203(4), 909 (2014)

    Article  Google Scholar 

  8. Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, Washington, DC (1991)

    Google Scholar 

  9. De Jong, K.: Learning with genetic algorithms: an overview. Mach. Learn. 3(2–3), 121–138 (1988)

    Article  Google Scholar 

  10. Deb, K., Agrawal, R.B., et al.: Simulated binary crossover for continuous search space. Complex Syst. 9(2), 115–148 (1995)

    MathSciNet  MATH  Google Scholar 

  11. Elter, M., Horsch, A.: Cadx of mammographic masses and clustered microcalcifications: a review. Med. Phy. 36(6Part1), 2052–2068 (2009)

    Google Scholar 

  12. Fenton, J.J., et al.: Influence of computer-aided detection on performance of screening mammography. N. Engl. J. Med. 356(14), 1399–1409 (2007)

    Article  Google Scholar 

  13. Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3(2), 95–99 (1988)

    Article  Google Scholar 

  14. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  15. Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  16. Holland, J.H.: Genetic algorithms. Sci. Am. 267(1), 66–73 (1992)

    Article  Google Scholar 

  17. Jamieson, A.R., Drukker, K., Giger, M.L.: Breast image feature learning with adaptive deconvolutional networks. In: Medical Imaging 2012: Computer-Aided Diagnosis, vol. 8315, pp. 64–76. SPIE (2012)

    Google Scholar 

  18. Jiang, J., Yao, B., Wason, A.: A genetic algorithm design for microcalcification detection and classification in digital mammograms. Comput. Med. Imaging Graph. 31(1), 49–61 (2007)

    Article  Google Scholar 

  19. Kooi, T., et al.: Large scale deep learning for computer aided detection of mammographic lesions. Med. Image Anal. 35, 303–312 (2017)

    Article  Google Scholar 

  20. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  21. Lee, R.S., Gimenez, F., Hoogi, A., Miyake, K.K., Gorovoy, M., Rubin, D.L.: A curated mammography data set for use in computer-aided detection and diagnosis research. Sci. Data 4(1), 1–9 (2017)

    Article  Google Scholar 

  22. Lehman, C.D., et al.: National performance benchmarks for modern screening digital mammography: update from the breast cancer surveillance consortium. Radiology 283(1), 49–58 (2017)

    Article  Google Scholar 

  23. Lehman, C.D., et al.: Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Int. Med. 175(11), 1828–1837 (2015)

    Google Scholar 

  24. Moreira, I.C., Amaral, I., Domingues, I., Cardoso, A., Cardoso, M.J., Cardoso, J.S.: Inbreast: toward a full-field digital mammographic database. Acad. Radiol. 19(2), 236–248 (2012)

    Article  Google Scholar 

  25. Oeffinger, K.C., et al.: Breast cancer screening for women at average risk: 2015 guideline update from the American cancer society. J. Am. Med. Assoc. 314(15), 1599–1614 (2015)

    Article  Google Scholar 

  26. Prügel-Bennett, A.: When a genetic algorithm outperforms hill-climbing. Theoret. Comput. Sci. 320(1), 135–153 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  27. Rodríguez-Ruiz, A., et al.: Detection of breast cancer with mammography: effect of an artificial intelligence support system. Radiology 290(2), 305–314 (2019)

    Article  MathSciNet  Google Scholar 

  28. Rodriguez-Ruiz, A., et al.: Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. J. Natl. Cancer Inst. 111(9), 916–922 (2019)

    Google Scholar 

  29. Sehgal, A.: Genetic algorithm as function optimizer in reinforcement learning and sensor odometry. Master’s thesis, University of Nevada, Reno (2019)

    Google Scholar 

  30. Sehgal, A., La, H., Louis, S., Nguyen, H.: Deep reinforcement learning using genetic algorithm for parameter optimization. In: 2019 Third IEEE International Conference on Robotic Computing (IRC), pp. 596–601. IEEE (2019)

    Google Scholar 

  31. Sehgal, A., Singandhupe, A., La, H.M., Tavakkoli, A., Louis, S.J.: Lidar-monocular visual odometry with genetic algorithm for parameter optimization. In: Bebis, G., et al. (eds.) ISVC 2019. LNCS, vol. 11845, pp. 358–370. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33723-0_29

    Chapter  Google Scholar 

  32. Sehgal, A., Ward, N., La, H., Louis, S.: Automatic parameter optimization using genetic algorithm in deep reinforcement learning for robotic manipulation tasks. arXiv preprint arXiv:2204.03656 (2022)

  33. Sehgal, A., Ward, N., La, H.M., Papachristos, C., Louis, S.: GA-DRL: genetic algorithm-based function optimizer in deep reinforcement learning for robotic manipulation tasks. arXiv preprint arXiv:2203.00141 (2022)

  34. Shen, L., Margolies, L.R., Rothstein, J.H., Fluder, E., McBride, R., Sieh, W.: Deep learning to improve breast cancer detection on screening mammography. Sci. Rep. 9(1), 1–12 (2019)

    Article  Google Scholar 

  35. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  36. Smith, R.A., et al.: American cancer society guidelines for breast cancer screening: update 2003. Cancer j. Clin. 53(3), 141–169 (2003)

    Google Scholar 

  37. Sun, Y., Babbs, C., Delp, E.: A comparison of feature selection methods for the detection of breast cancers in mammograms: adaptive sequential floating search vs. genetic algorithm. In: 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 6532–6535. IEEE (2006)

    Google Scholar 

  38. Timmers, J., et al.: The breast imaging reporting and data system (BI-RADS) in the Dutch breast cancer screening programme: its role as an assessment and stratification tool. Eur. Radiol. 22(8), 1717–1723 (2012)

    Article  Google Scholar 

  39. Verma, B., Zhang, P.: A novel neural-genetic algorithm to find the most significant combination of features in digital mammograms. Appl. Soft Comput. 7(2), 612–625 (2007)

    Article  Google Scholar 

  40. Wahde, M.: Biologically Inspired Optimization Methods: An Introduction. WIT Press (2008)

    Google Scholar 

  41. Wang, D., Khosla, A., Gargeya, R., Irshad, H., Beck, A.H.: Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:1606.05718 (2016)

  42. Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: Cluster computing with working sets. In: 2nd USENIX Workshop on Hot Topics in Cloud Computing, (HotCloud 2010) (2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Advanced Robotics and Automation (ARA) Laboratory, Reno, USA

    Adarsh Sehgal & Hung Manh La

  2. Department of Computer Science and Engineering, University of Nevada, Reno, 89557, NV, USA

    Adarsh Sehgal, Hung Manh La & George Bebis

  3. Reno, NV, USA

    Muskan Sehgal

Authors
  1. Adarsh Sehgal
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  2. Muskan Sehgal
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  3. Hung Manh La
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  4. George Bebis
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Corresponding author

Correspondence to Adarsh Sehgal .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. University of Illinois Urbana-Champaign, Urbana, IL, USA

    Bo Li

  3. National University of Singapore, Singapore, Singapore

    Angela Yao

  4. Microsoft Research Asia, Beijing, China

    Yang Liu

  5. University of Missouri, Columbia, MO, USA

    Ye Duan

  6. City University of Hong Kong, Kowloon, Hong Kong

    Manfred Lau

  7. Idaho National Laboratory, Idaho Falls, ID, USA

    Rajiv Khadka

  8. Salesforce, Seattle, WA, USA

    Ana Crisan

  9. Tufts University, Medford, MA, USA

    Remco Chang

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Sehgal, A., Sehgal, M., La, H.M., Bebis, G. (2022). Deep Learning Hyperparameter Optimization for Breast Mass Detection in Mammograms. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2022. Lecture Notes in Computer Science, vol 13599. Springer, Cham. https://doi.org/10.1007/978-3-031-20716-7_21

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  • DOI: https://doi.org/10.1007/978-3-031-20716-7_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20715-0

  • Online ISBN: 978-3-031-20716-7

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