Skip to main content

Advertisement

SpringerLink
Log in

HihO: accelerating artificial intelligence interpretability for medical imaging in IoT applications using hierarchical occlusion

Opening the black box

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In the medical imaging domain, nonlinear warping has enabled pixel-by-pixel mapping of one image dataset to a reference dataset. This co-registration of data allows for robust, pixel-wise, statistical maps to be developed in the domain, leading to new insights regarding disease mechanisms. Deep learning technologies have given way to some impressive discoveries. In some applications, deep learning algorithms have surpassed the abilities of human image readers to classify data. As long as endpoints are clearly defined, and the input data volume is large enough, deep learning networks can often converge and reach prediction, classification and segmentation with success rates as high or higher than human operators. However, machine learning and deep learning algorithms are complex. Interpretability is not always a product of the classifications performed. Visualization techniques have been developed to add a layer of interpretability. The work presented here builds on a framework for augmenting statistical findings in medical imaging workflows with machine learning results. Utilizing the framework, visualization techniques for the machine learning portion are compared in an application, and a novel, lightweight technique for machine learning visualization is proposed as a means of increasing the portability of machine learning interpretability to Internet of Things applications. The novel visualization, hierarchical occlusion, can improve time to visualization by three orders of magnitude over a traditional occlusion sensitivity algorithm.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Monroe WS, Anthony T, Tanik MM, Skidmore FM (2019) Towards a framework for validating machine learning results in medical imaging: opening the black box. In: Proceedings of the practice and experience in advanced research computing on rise of the machines (learning). ACM, p 74

  2. Chattopadhay A, Sarkar A, Howlader P, Balasubramanian VN (2018) Grad-cam++: generalized gradient-based visual explanations for deep convolutional networks. In: 2018 IEEE winter conference on applications of computer vision (WACV). IEEE, p 839–847

  3. Lundberg SM , Lee S-I (2017) A unified approach to interpreting model predictions. In: Advances in neural information processing systems, pp 4765–4774

  4. Grover S, Bhartia S, Yadav A, Seeja KR et al (2018) Predicting severity of Parkinson’s disease using deep learning. Procedia Comput Sci 132:1788–1794

    Article  Google Scholar 

  5. Zaharchuk G, Gong E, Wintermark M, Rubin D, Langlotz CP (2018) Deep learning in neuroradiology. Am J Neuroradiol 39(10):1776–1784

    Article  Google Scholar 

  6. Lipton ZC (2016) The mythos of model interpretability. arXiv preprint arXiv:1606.03490

  7. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Laak JAVD, Van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88

    Article  Google Scholar 

  8. Sculley D, Gary H, Daniel G, Eugene D, Todd P, Dietmar E, Vinay C, Michael Y (2014) Machine learning: the high interest credit card of technical debt. SE4ML: software engineering for machine learning (NIPS 2014 Workshop)

  9. Selvaraju RR, Das A, Vedantam R, Cogswell M, Parikh D, Batra D (2016) Grad-cam: Why did you say that? Visual explanations from deep networks via gradient-based localization. CoRR, abs/1610.02391, 7

  10. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D , Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE international conference on computer vision (ICCV). IEEE, pp 618–626

  11. Yang C, Rangarajan A, Ranka S (2018) Visual explanations from deep 3D convolutional neural networks for alzheimer’s disease classification. arXiv preprint arXiv:1803.02544

  12. Anthony T, Robinson JP, Marstrander JR, Brook GR, Horton M, Skidmore FM (2017) High performance/throughput computing workflow for a neuro-imaging application: provenance and approaches. In: Suh SC, Anthony T (eds) Big data and visual analytics. Springer, pp 245–256

  13. Kraguljac NV, Anthony T, Skidmore FM, Marstrander J, Morgan CJ, Reid MA, White DM, Jindal RD, Skefos NHM, Lahti AC (2019) Micro-and macrostructural white matter integrity in never-treated and currently unmedicated patients with schizophrenia and effects of short term antipsychotic treatment. Biol Psychiatry Cogn Neurosci Neuroimaging 4:462–471

    Google Scholar 

  14. Yin J, Anthony T, Marstrander J, Liu Y, Burdyshaw C, Horton M, Crosby L, Glenn BR, Skidmore F (2016) Optimization of non-linear image registration in AFNI. In: Proceedings of the XSEDE16 conference on diversity, big data, and science at scale. ACM, p 6

  15. Wang L, Wu Z, Karanam S, Peng K-C, Singh RV (2018) Reducing visual confusion with discriminative attention. arXiv preprint arXiv:1811.07484

  16. Scarpato N, Pieroni A, Di Nunzio L, Fallucchi F (2017) E-health-IoT universe: a review. Management 21(44):46

    Google Scholar 

  17. García-Magariño I, Muttukrishnan R, Lloret J (2019) Human-centric ai for trustworthy iot systems with explainable multilayer perceptrons. IEEE Access 7:125562–125574

    Article  Google Scholar 

  18. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  19. Marek K, Jennings D, Lasch S, Siderowf A, Tanner C, Simuni T, Coffey C, Kieburtz K, Flagg E, Chowdhury S et al (2011) The parkinson progression marker initiative (ppmi). Prog Neurobiol 95(4):629–635

    Article  Google Scholar 

  20. Arfanakis K, Fleischman DA, Grisot G, Barth CM, Varentsova A, Morris MC, Barnes LL, Bennett DA (2013) systemic inflammation in non-demented elderly human subjects: brain microstructure and cognition. PLoS ONE 8(8):e73107

    Article  Google Scholar 

  21. Skidmore FM, Monroe WS, Hurt C, Nicholas AP, Gerstenecker A, Anthony T, Jololian L, Cutter G, Bashir A, Denny T, et al (2019) The emerging gait dysfunction phenotype in idiopathic parkinson’s disease. bioRxiv, pp 632638

  22. Jenkinson M, Christian FB, Timothy EJB, Mark WW, Stephen MSF (2012) FSL. Neuroimage 62(2):782–790

    Article  Google Scholar 

  23. Cox RW (1996) Afni: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29(3):162–173

    Article  Google Scholar 

  24. Pan SJ, Yang Q et al (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359

    Article  Google Scholar 

  25. Chollet F (2015) keras, GitHub. https://github.com/fchollet/keras

  26. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M et al (2016) Tensorflow: a system for large-scale machine learning. OSDI 16:265–283

    Google Scholar 

  27. Kotikalapudi R (2017) keras-vis, Github. https://github.com/raghakot/keras-vis

  28. Petsiuk V, Das A, Saenko K (2018) Rise: randomized input sampling for explanation of black-box models. arXiv preprint arXiv:1806.07421

  29. Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In :European conference on computer vision. Springer, pp 818–833

  30. Dourado CMJM Jr, da Suane Pires P, Silva RV, da Nóbrega M, Carlos A, da Barros S, Pedro P, Filho R, de Albuquerque VHC (2019) Deep learning IoT system for online stroke detection in skull computed tomography images. Comput Netw 152:25–39

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge Dr. U. John Tanik for making us aware of the special issue on this topic. The author’s would also like to acknowledge Thomas Anthony, for providing an initial review of the HihO results and encouraging this work.

Author information

Authors and Affiliations

  1. IT Research Computing, University of Alabama at Birmingham, Birmingham, USA

    William S. Monroe

  2. Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, USA

    Frank M. Skidmore, David G. Odaibo & Murat M. Tanik

Authors
  1. William S. Monroe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Frank M. Skidmore
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David G. Odaibo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Murat M. Tanik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to William S. Monroe.

Ethics declarations

Conflict of interest

The authors share no conflict of interest with the submission of this work. This manuscript represents the authors’ original work and has neither published nor has it been submitted simultaneously elsewhere.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Monroe, W.S., Skidmore, F.M., Odaibo, D.G. et al. HihO: accelerating artificial intelligence interpretability for medical imaging in IoT applications using hierarchical occlusion. Neural Comput & Applic 33, 6027–6038 (2021). https://doi.org/10.1007/s00521-020-05379-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05379-4

Keywords

Search

Navigation