Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Data Characterization for Reliable AI in Medicine

  • Conference paper
  • First Online:
Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1704))

  • 598 Accesses

Abstract

Research in Artificial Intelligence (AI)-based medical computer vision algorithms bear promises to improve disease screening, diagnosis, and subsequently patient care. However, these algorithms are highly impacted by the characteristics of the underlying data. In this work, we discuss various data characteristics, namely Volume, Veracity, Validity, Variety, and Velocity, that impact the design, reliability, and evolution of machine learning in medical computer vision. Further, we discuss each characteristic and the recent works conducted in our research lab that informed our understanding of the impact of these characteristics on the design of medical decision-making algorithms and outcome reliability.

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
$34.99 /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
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • 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. Alzubaidi, L., et al.: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8(1), 1–74 (2021). https://doi.org/10.1186/s40537-021-00444-8

    Article  Google Scholar 

  2. Suzuki, K.: Overview of deep learning in medical imaging. Radiol. Phys. Technol. 10(3), 257–273 (2017). https://doi.org/10.1007/s12194-017-0406-5

    Article  Google Scholar 

  3. Younas, M.: Research challenges of big data. SOCA 13(2), 105–107 (2019). https://doi.org/10.1007/s11761-019-00265-x

    Article  Google Scholar 

  4. Ganesan, P., Rajaraman, S., Long, R., Ghoraani, B., Antani, S.: Assessment of data augmentation strategies toward performance improvement of abnormality classification in chest radiographs. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2019). https://doi.org/10.1109/EMBC.2019.8857516

  5. Yang, F., et al.: Differentiating between drug-sensitive and drug-resistant tuberculosis with machine learning for clinical and radiological features. Quant. Imaging Med. Surg. 12, 675–687 (2022). https://doi.org/10.21037/qims-21-290

  6. Yang, F., et al.: Automated drug-resistant TB screening: importance of demographic features and radiological findings in chest X-ray. In: Proceedings of Applied Imagery Pattern Recognition Workshop, 9–12 October 2021. https://doi.org/10.1109/AIPR52630.2021.9762198

  7. Zamzmi, G., Rajaraman, S., Antani, S.: Accelerating super-resolution and visual task analysis in medical images. Appl. Sci. 10 (2020). https://doi.org/10.3390/app10124282

  8. Zamzmi, G., Rajaraman, S., Antani, S.: UMS-Rep: unified modality-specific representation for efficient medical image analysis. Inform. Med. Unlocked. 24, 100571 (2021). https://doi.org/10.1016/j.imu.2021.100571

    Article  Google Scholar 

  9. Angara, S., Guo, P., Xue, Z., Antani, S.: Semi-supervised learning for cervical precancer detection. In: Proceedings of International Symposium on Computer-Based Medical Systems, pp. 202–206, June 2021. https://doi.org/10.1109/CBMS52027.2021.00072

  10. Pal, A., Xue, Z., Antani, S.: Deep cervix model development from heterogeneous and partially labeled image datasets. In: Proceedings of the 7th International Conference on Emerging Applications of Information Technology (EAIT 2022), Kolkata, India, 30–31 March 2022

    Google Scholar 

  11. Pal, A., et al.: Deep multiple-instance learning for abnormal cell detection in cervical histopathology images. Comput. Biol. Med. 138, 104890 (2021). https://doi.org/10.1016/j.compbiomed.2021.104890

    Article  Google Scholar 

  12. Rajaraman, S., Zamzmi, G., Folio, L.R., Antani, S.: Detecting tuberculosis-consistent findings in lateral chest X-rays using an ensemble of CNNs and vision transformers. Front. Genet. 13, 1–13 (2022). https://doi.org/10.3389/fgene.2022.864724

    Article  Google Scholar 

  13. Zamzmi, G., Oguguo, T., Rajaraman, S., Antani, S.: Open world active learning for echocardiography view classification. In: Proceedings of SPIE Medical Imaging (2022): Computer-Aided Diagnosis, vol. 120330J, 4 April 2022. https://doi.org/10.1117/12.2612578

  14. Rajaraman, S., Sornapudi, S., Alderson, P.O., Folio, L.R., Antani, S.K.: Analyzing inter-reader variability affecting deep ensemble learning for COVID-19 detection in chest radiographs. PLoS ONE (2020). https://doi.org/10.1371/journal.pone.0242301

  15. Yang, F., et al.: Annotations of lung abnormalities in the Shenzhen chest pulmonary diseases. MDPI Data 7(7), 95 (2022). https://doi.org/10.3390/data7070095

    Article  Google Scholar 

  16. Guo, P., et al.: Ensemble deep learning for cervix image selection toward improving reliability in automated cervical precancer screening. Diagnostics 10 (2020). https://doi.org/10.3390/diagnostics10070451

  17. Xue, Z., et al.: Cleaning highly unbalanced multisource image dataset for quality control in cervical precancer screening. In: Santosh, K.C., Hegadi, R., Pal, U. (eds.) RTIP2R 2021. CCIS, vol 1576, pp. 3–13. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-07005-1_1

  18. Xue, Z., Angara, S., Levitz, D., Antani, S.K.: Analysis of digital noise reduction methods on classifiers used in automated visual evaluation. In: SPIE International Society of Optical Engineering, p. 28 (2022). https://doi.org/10.1117/12.2610235

  19. Xue, Z., et al.: Image quality classification for automated visual evaluation of cervical precancer. In: Zamzmi, G., Antani, S., Bagci, U., Linguraru, M.G., Rajaraman, S., Xue, Z. (eds.) MILLanD 2022. LNCS, vol. 13559, pp. 206–217. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16760-7_20

  20. Rajaraman, S., Antani, S.: Weakly labeled data augmentation for deep learning: a study on COVID-19 detection in chest X-rays. Diagnostics 10, 1–17 (2020). https://doi.org/10.3390/diagnostics10060358

  21. Xue, Z., et al.: A deep clustering method for analyzing uterine cervix images across imaging devices. In: Proceedings of the IEEE Symposium on Computer-Based Medical Systems, pp. 527–532, June 2021. https://doi.org/10.1109/CBMS52027.2021.00085

  22. Rodríguez, A.C., et al.: Cervical cancer incidence after screening with HPV, cytology, and visual methods: 18-year follow-up of the Guanacaste cohort. Int. J. Cancer. 140, 1926–1934 (2017). https://doi.org/10.1002/ijc.30614

    Article  Google Scholar 

  23. Xue, Z., et al.: A demonstration of automated visual evaluation of cervical images taken with a smartphone camera. Int. J. Cancer 147, 2416–2423 (2020). https://doi.org/10.1002/ijc.33029

    Article  Google Scholar 

  24. Zamzmi, G., Rajaraman, S., Hsu, L.-Y., Sachdev, V., Antani, S.: Real-time echocardiography image analysis and quantification of cardiac indices. Med. Image Anal. 80, 102438 (2022). https://doi.org/10.1016/j.media.2022.102438

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by the Intramural Research Program (IRP) of the National Library of Medicine (NLM), National Institutes of Health (NIH).

Author information

Authors and Affiliations

  1. Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA

    Sivaramakrishnan Rajaraman, Ghada Zamzmi, Feng Yang, Zhiyun Xue & Sameer K. Antani

Authors
  1. Sivaramakrishnan Rajaraman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ghada Zamzmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Feng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhiyun Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sameer K. Antani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sameer K. Antani .

Editor information

Editors and Affiliations

  1. University of South Dakota, Vermillion, SD, USA

    KC Santosh

  2. Texas A&M University, College Station, TX, USA

    Ayush Goyal

  3. University of Luxembourg, Luxembourg, Luxembourg

    Djamila Aouada

  4. University of Derby, Derby, UK

    Aaisha Makkar

  5. University of Minnesota, Minneapolis, MN, USA

    Yao-Yi Chiang

  6. IIIT Allahabad, Allahabad, India

    Satish K Singh

Ethics declarations

The authors declare no conflict of interest.

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rajaraman, S., Zamzmi, G., Yang, F., Xue, Z., Antani, S.K. (2023). Data Characterization for Reliable AI in Medicine. In: Santosh, K., Goyal, A., Aouada, D., Makkar, A., Chiang, YY., Singh, S.K. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2022. Communications in Computer and Information Science, vol 1704. Springer, Cham. https://doi.org/10.1007/978-3-031-23599-3_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-23599-3_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23598-6

  • Online ISBN: 978-3-031-23599-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics