[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

TensorFlow for Doctors

  • Conference paper
  • First Online:
Information Management and Big Data (SIMBig 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1070))

Included in the following conference series:

Abstract

Machine learning has advanced substantially in the past few years, and there are many generic solutions freely available to classify text and images. The solutions are so straightforward to set up and run that having a software background is no longer necessary to perform machine learning experimentation. These systems are being adapted in many ways, and it seems only natural that those in the medical field may wish to see how machine learning might help with their research. This research examines if off-the-shelf machine learning systems are suitable for research by medical professionals who do not have software backgrounds. If all doctors who wish to experiment with machine learning could have an adequate system available, the impact on research could be substantial. This investigation applies a commonly available machine learning practice lab to medical images. As part of this investigation, we evaluated the TensorFlow for Poets (TFP) tutorial from Google Code Labs with openly available medical images provided by Kaggle Inc. While we would not recommend our test results as a basis for diagnosing medical conditions, the results were encouraging enough to suggest that using off-the-shelf systems can offer a promising opportunity to expand machine learning research into those with medical, but not software backgrounds.

Supported by the University of St. Thomas.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 35.99
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 44.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D.L., Erickson, B.J.: Deep learning for brain MRI segmentation: state of the art and future directions. J. Digit. Imaging 30(4), 449–459 (2017)

    Article  Google Scholar 

  2. Aswathy, S., Dhas, G.G.D., Kumar, S.: A survey on detection of brain tumor from MRI brain images. In: 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), pp. 871–877. IEEE (2014)

    Google Scholar 

  3. Bhooshan, N., et al.: Potential of computer-aided diagnosis of high spectral and spatial resolution (HISS) MRI in the classification of breast lesions. J. Magn. Reson. Imaging 39(1), 59–67 (2014)

    Article  Google Scholar 

  4. Bibin, D., Nair, M.S., Punitha, P.: Malaria parasite detection from peripheral blood smear images using deep belief networks. IEEE Access 5, 9099–9108 (2017)

    Article  Google Scholar 

  5. Brownlee, J.: A gentle introduction to k-fold cross-validation (2018). https://machinelearningmastery.com/k-fold-cross-validation/. Accessed 4 July 2019

  6. Calderón-Contreras, J.D., Chacón-Murguía, M.I., Villalobos-Montiel, A.J., Ortega-Máynez, L.: A fuzzy computer aided diagnosis system using breast thermography. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp. 105–108. IEEE (2015)

    Google Scholar 

  7. CDC: Causes of pneumonia (2020). https://www.cdc.gov/pneumonia/causes.html

  8. CDC: Treatment of malaria: Guidelines for clinicians (2020). https://www.cdc.gov/malaria/diagnosis_treatment/clinicians1.html

  9. Chakrabarty, N.: Brain MRI images for brain tumor detection (2019). https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection, Accessed 4 July 2019

  10. Chmielewski, A., Dufort, P., Scaranelo, A.M.: A computerized system to assess axillary lymph node malignancy from sonographic images. Ultrasound Med. Biol. 41(10), 2690–2699 (2015)

    Article  Google Scholar 

  11. Mayo Clinic: Brain tumor (2020). https://www.mayoclinic.org/diseases-conditions/brain-tumor/symptoms-causes/syc-20350084

  12. Mayo Clinic: Malaria (2020). https://www.mayoclinic.org/diseases-conditions/malaria/symptoms-causes/syc-20351184

  13. Coleman, R.E., et al.: Comparison of field and expert laboratory microscopy for active surveillance for asymptomatic plasmodium falciparum and plasmodium vivax in western thailand. Am. J. Trop. Med. Hyg. 67(2), 141–144 (2002)

    Article  Google Scholar 

  14. Cunningham, P., Delany, S.J.: k-nearest neighbour classifiers. Multiple Classif. Syst. 34(8), 1–17 (2007)

    Google Scholar 

  15. Das, D.K., Ghosh, M., Pal, M., Maiti, A.K., Chakraborty, C.: Machine learning approach for automated screening of malaria parasite using light microscopic images. Micron 45, 97–106 (2013)

    Article  Google Scholar 

  16. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

  17. Fernández-Carrobles, M.M., Bueno, G., Déniz, O., Salido, J., García-Rojo, M., González-López, L.: A CAD system for the acquisition and classification of breast TMA in pathology. Stud. Health Technol. Inform 210, 756–60 (2015)

    Google Scholar 

  18. Gardezi, S.J.S., Faye, I., Sanchez Bornot, J.M., Kamel, N., Hussain, M.: Mammogram classification using dynamic time warping. Multimed. Tools Appl. 77(3), 3941–3962 (2017). https://doi.org/10.1007/s11042-016-4328-8

    Article  Google Scholar 

  19. Gitonga, L., Memeu, D.M., Kaduki, K.A., Kale, M.A.C., Muriuki, N.S.: Determination of plasmodium parasite life stages and species in images of thin blood smears using artificial neural network. Open J. Clin. Diagn. 4(02), 78 (2014)

    Article  Google Scholar 

  20. Google (2019). https://www.tensorflow.org/about. Accessed 7 July 2019

  21. Google (2019). https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/. Accessed 7 July 2019

  22. Havaei, M., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)

    Article  Google Scholar 

  23. Khan, S., Hussain, M., Aboalsamh, H., Bebis, G.: A comparison of different Gabor feature extraction approaches for mass classification in mammography. Multimed. Tools Appl. 76(1), 33–57 (2017)

    Article  Google Scholar 

  24. Kudo, T., Matsumoto, Y.: Chunking with support vector machines. In: Proceedings of the Second Meeting of the North American Chapter of the Association for Computational Linguistics on Language Technologies, pp. 1–8. Association for Computational Linguistics (2001)

    Google Scholar 

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

    Article  Google Scholar 

  26. Lo, C.M., Moon, W.K., Huang, C.S., Chen, J.H., Yang, M.C., Chang, R.F.: Intensity-invariant texture analysis for classification of BI-RADS category 3 breast masses. Ultrasound Med. Biol. 41(7), 2039–2048 (2015)

    Article  Google Scholar 

  27. Loukas, C., Kostopoulos, S., Tanoglidi, A., Glotsos, D., Sfikas, C., Cavouras,D.: Breast cancer characterization based on image classification of tissue sections visualized under low magnification. Comput. Math. Methods Med. 2013, 3 (2013)

    Google Scholar 

  28. Makkapati, V.V., Rao, R.M.: Segmentation of malaria parasites in peripheral blood smear images. In: 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1361–1364. IEEE (2009)

    Google Scholar 

  29. Mooney, P.: Chest x-ray images (2018). https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia. Accessed 4 July 2019

  30. National Heart, Lung, and Blood Institute: Pneumonia (2020). https://www.nhlbi.nih.gov/health-topics/pneumonia

  31. National Library of Medicine: Malaria cell images dataset (2019). https://ceb.nlm.nih.gov/repositories/malaria-datasets/. Accessed 4 July 2019

  32. Palaniappan, R., Sundaraj, K., Ahamed, N.U.: Machine learning in lung sound analysis: a systematic review. Biocybern. Biomedi. Eng. 33(3), 129–135 (2013)

    Article  Google Scholar 

  33. Pankratz, D.G., et al.: Usual interstitial pneumonia can be detected in transbronchial biopsies using machine learning. Ann. Am. Thorac. Soc. 14(11), 1646–1654 (2017)

    Article  Google Scholar 

  34. Purwar, Y., Shah, S.L., Clarke, G., Almugairi, A., Muehlenbachs, A.: Automated and unsupervised detection of malarial parasites in microscopic images. Malaria J. 10(1), 364 (2011)

    Article  Google Scholar 

  35. Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)

    Google Scholar 

  36. Rajpurkar, P., et al.: ChexNet: radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225 (2017)

  37. Roth, M.Y., Elmore, J.G., Yi-Frazier, J.P., Reisch, L.M., Oster, N.V., Miglioretti, D.L.: Self-detection remains a key method of breast cancer detection for US women. J. Women’s Health 20(8), 1135–1139 (2011)

    Article  Google Scholar 

  38. Schaefer, G., Nakashima, T.: Strategies for addressing class imbalance in ensemble classification of thermography breast cancer features. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 2362–2367. IEEE (2015)

    Google Scholar 

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

    Google Scholar 

  40. Spanhol, F., de Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification (2015). https://www.kaggle.com/ankur1809/breakhist-dataset. Accessed 4 July 2019

  41. Tek, F.B., Dempster, A.G., Kale, I.: Parasite detection and identification for automated thin blood film malaria diagnosis. Comput. Vis. Image Underst. 114(1), 21–32 (2010)

    Article  Google Scholar 

  42. Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: ChestX-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2097–2106 (2017)

    Google Scholar 

  43. Weiss, W.A., Medved, M., Karczmar, G.S., Giger, M.L.: Residual analysis of the water resonance signal in breast lesions imaged with high spectral and spatial resolution (HISS) MRI: a pilot study. Med. Phys. 41(1), 012303 (2014)

    Article  Google Scholar 

  44. Yao, L., Poblenz, E., Dagunts, D., Covington, B., Bernard, D., Lyman, K.: Learning to diagnose from scratch by exploiting dependencies among labels. arXiv preprint arXiv:1710.10501 (2017)

  45. Yassin, N.I., Omran, S., El Houby, E.M., Allam, H.: Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: a systematic review. Comput. Methods Programs Biomed. 156, 25–45 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of St. Thomas, St. Paul, MN, 55105, USA

    Isha Agarwal, Rajkumar Kolakaluri & Michael Dorin

  2. Universidad del Pacífico, Lima, Peru

    Mario Chong

Authors
  1. Isha Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajkumar Kolakaluri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael Dorin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mario Chong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael Dorin .

Editor information

Editors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Juan Antonio Lossio-Ventura

  2. University of A Coruña, A Coruña, Spain

    Nelly Condori-Fernandez

  3. Visibilia, São Paulo, Brazil

    Jorge Carlos Valverde-Rebaza

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Agarwal, I., Kolakaluri, R., Dorin, M., Chong, M. (2020). TensorFlow for Doctors. In: Lossio-Ventura, J.A., Condori-Fernandez, N., Valverde-Rebaza, J.C. (eds) Information Management and Big Data. SIMBig 2019. Communications in Computer and Information Science, vol 1070. Springer, Cham. https://doi.org/10.1007/978-3-030-46140-9_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-46140-9_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-46139-3

  • Online ISBN: 978-3-030-46140-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation