Skip to main content

Preliminary Study on Visual Attention Maps of Experts and Nonexperts When Examining Pathological Microscopic Images

  • Conference paper
  • First Online:
Digital TV and Wireless Multimedia Communication (IFTC 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1181))

  • 599 Accesses

Abstract

Pathological microscopic image is regarded as a gold standard for the diagnosis of disease, and eye tracking technology is considered as a very effective tool for medical education. It will be very interesting if we use the eye tracking to predict where pathologists or doctors and persons with no or little experience look at the pathological microscopic image. In the current work, we first establish a pathological microscopic image database with the eye movement data of experts and nonexperts (PMIDE), including a total of 425 pathological microscopic images. The statistical analysis is afterwards conducted on PMIDE to analyze the difference in eye movement behavior between experts and nonexperts. The results show that although there is no significant difference in general, the experts focus on a broader scope than nonexperts. This inspires us to respectively develop saliency models for experts and nonexperts. Furthermore, the existing 10 saliency models are tested on PMIDE, and the performance of these models are all unacceptable with AUC, CC, NSS and SAUC below 0.73, 0.47, 0.78 and 0.52, respectively. This study indicates that the saliency models specific to pathological microscopic images urgent need to be developed using our databaseā€”PMIDE or the other related databases.

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

Access this chapter

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

References

  1. Glaser, A.K., et al.: Light-sheet microscopy for slide-free non-destructive pathology of large clinical specimens. Nat. Biomed. Eng. 1(7), 0084 (2017)

    ArticleĀ  Google ScholarĀ 

  2. Mohapatra, S., et al.: Blood microscopic image segmentation using rough sets. In: 2011 International Conference on Image Information Processing. IEEE (2011)

    Google ScholarĀ 

  3. Itti, L., et al.: Computational modelling of visual attention. Nat. Rev. Neurosci. 2(3), 194 (2001)

    ArticleĀ  Google ScholarĀ 

  4. Cornish, L., et al.: Eye-tracking reveals how observation chart design features affect the detection of patient deterioration: an experimental study. Appl. Ergon. 75, 230ā€“242 (2019)

    ArticleĀ  Google ScholarĀ 

  5. LĆ©vĆŖque, L., et al.: State of the art: eye-tracking studies in medical imaging. IEEE Access 6, 37023ā€“37034 (2018)

    ArticleĀ  Google ScholarĀ 

  6. Duan, H., et al.: Learning to predict where the children with ASD look. In: 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, pp. 704ā€“708 (2018)

    Google ScholarĀ 

  7. Li, R., et al.: Modeling eye movement patterns to characterize perceptual skill in image-based diagnostic reasoning processes. Comput. Vis. Image Underst. 151, 138ā€“152 (2016)

    ArticleĀ  Google ScholarĀ 

  8. Van der Gijp, A., et al.: How visual search relates to visual diagnostic performance: a narrative systematic review of eye-tracking research in radiology. Adv. Health Sci. Educ. 22(3), 765ā€“787 (2017)

    ArticleĀ  Google ScholarĀ 

  9. Liu, H., et al.: Visual attention in objective image quality assessment: based on eye-tracking data. IEEE Trans. Circuits Syst. Video Technol. 21(7), 971ā€“982 (2011)

    ArticleĀ  Google ScholarĀ 

  10. Min, X., et al.: Fixation prediction through multimodal analysis. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 13(1), 6 (2017)

    Google ScholarĀ 

  11. Min, X., et al.: Visual attention analysis and prediction on human faces. Inf. Sci. 420, 417ā€“430 (2017)

    ArticleĀ  Google ScholarĀ 

  12. Gu, K., et al.: Visual saliency detection with free energy theory. IEEE Signal Process. Lett. 22(10), 1552ā€“1555 (2015)

    ArticleĀ  Google ScholarĀ 

  13. Bylinskii, Z., et al.: What do different evaluation metrics tell us about saliency models? IEEE Trans. Pattern Anal. Mach. Intell. 41(3), 740ā€“757 (2019)

    ArticleĀ  Google ScholarĀ 

  14. Bylinskii, Z., et al.: Mit saliency benchmark, vol. 12, p. 13 (2014/2015). http://saliency.mit.edu/resultsmit300.html

  15. Walther, D., et al.: Modeling attention to salient proto-objects. Neural Netw. 19, 1395ā€“1407 (2006)

    ArticleĀ  Google ScholarĀ 

  16. Bruce, N.D.B., et al.: Saliency based on information maximization. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 155ā€“162 (2005)

    Google ScholarĀ 

  17. Seo, H.J., et al.: Static and space-time visual saliency detection by self-resemblance. J. Vis. 9(12), 15, 1ā€“27 (2009)

    ArticleĀ  Google ScholarĀ 

  18. Goferman, S., et al.: Context-aware saliency detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 1915ā€“1926 (2012)

    ArticleĀ  Google ScholarĀ 

  19. Zhang, L., et al.: SUN: a Bayesian framework for saliency using natural statistics. J. Vis. 8(7), 1ā€“20 (2008)

    ArticleĀ  Google ScholarĀ 

  20. Harel, J., et al.: Graph-based visual saliency. In: Advances in Neural Information Processing Systems (2007)

    Google ScholarĀ 

  21. Hou, X., et al.: Dynamic attention: searching for coding length increments. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 681ā€“688 (2008)

    Google ScholarĀ 

  22. Garcia-Diaz, A., Fdez-Vidal, X.R., Pardo, X.M., Dosil, R.: Decorrelation and distinctiveness provide with human-like saliency. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2009. LNCS, vol. 5807, pp. 343ā€“354. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04697-1_32

    ChapterĀ  Google ScholarĀ 

  23. Hou, et al.:Saliency detection: a spectral residual approach. In 2007 IEEE Conference on Computer Vision and Pattern Recognition. Ieee (2007)

    Google ScholarĀ 

  24. Judd, T., et al.: Learning to predict where humans look. In: 2009 IEEE 12th International Conference on Computer Vision. IEEE, pp. 2106ā€“2113 (2009)

    Google ScholarĀ 

  25. Harel, J., et al.: Graph-based visual saliency. In: Advances in Neural Information Processing Systems, pp. 545ā€“552 (2007)

    Google ScholarĀ 

  26. Goferman, S., et al.: Context-aware saliency detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 1915ā€“1926 (2011)

    ArticleĀ  Google ScholarĀ 

Download references

Acknowledgement

This work is sponsored by the Shanghai Sailing Program (No. 19YF1414100), the National Natural Science Foundation of China (No. 61831015, No. 61901172), the STCSM (No. 18DZ2270700), and the China Postdoctoral Science Foundation funded project (No. 2016M600315).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Menghan Hu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

Ā© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yu, W., Hu, M., Xu, S., Li, Q. (2020). Preliminary Study on Visual Attention Maps of Experts and Nonexperts When Examining Pathological Microscopic Images. In: Zhai, G., Zhou, J., Yang, H., An, P., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2019. Communications in Computer and Information Science, vol 1181. Springer, Singapore. https://doi.org/10.1007/978-981-15-3341-9_12

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-3341-9_12

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3340-2

  • Online ISBN: 978-981-15-3341-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics