Improving human-machine cooperative visual search with soft highlighting

ACM Transactions on Applied Perception

Volumn 15, Issue 1, 2017, Pages

  Kneusel, Ronald T ^a   Mozer, Michael C ^a  

^a University of Colorado at Boulder   (United States)

Author keywords

Soft highlighting; Target localization; Visual search

Indexed keywords

LEARNING SYSTEMS; SATELLITE IMAGERY;

AUTOMATIC CLASSIFIERS; CONFIDENCE LEVELS; HUMAN PERFORMANCE; HUMAN-LEVEL PERFORMANCE; OBJECT IDENTIFICATION; SOFT HIGHLIGHTING; TARGET LOCALIZATION; VISUAL SEARCH;

VISION;

EID: 85032930126     PISSN: 15443558     EISSN: 15443965     Source Type: Journal    
DOI: 10.1145/3129669     Document Type: Article

References (38)

1. E. Alberdi, A. Povyakalo, L. Strigini, and P. Ayton. 2004. Effects of incorrect computer-aided detection (CAD) output on human decisionmaking in mammography. Acad. Radiol. 11, 8 (2004), 909-918.
2. J. R. Anderson. 1971. Land-use classification schemes. Photogram. Eng. 37, 4 (1971), 379-387.
3. C. Balleyguier, K. Kinkel, J. Fermanian, S. Malan, G. Djen, P. Taourel, and O. Helenon. 2005. Computer-aided detection (CAD) in mammography: Does it help the junior or the senior radiologist? Eur. J. Radiol. 54, 1 (2005), 90-96.
4. I. Bankman. 2008. Handbook of Medical Image Processing and Analysis, 2nd ed. Academic Press, San Diego, CA.
5. L. Bastin, P. F. Fisher, and J. Wood. 2002. Visualizing uncertainty in multi-spectral remotely sensed imagery. Comput. Geosci. 28, 3 (2002), 337-350.
6. H.-D. Cheng, X. Cai, X. Chen, L. Hu, and X. Lou. 2003. Computer-aided detection and classification of microcalcifications in mammograms: A survey. Pattern Recogn. 36, 12 (2003), 2967-2991.
7. C. A. Cunningham, T. Drew, and J. M. Wolfe. 2016. Analog computer-aided detection (CAD) information can be more effective than binary marks (2016). Submitted for publication.
8. C. J. D'Orsi. 2001. Computer-aided detection: There is no free lunch. Radiology 221, 3 (2001), 585-586.
9. T. Drew, C. Cunningham, and J. M.Wolfe. 2012. When and why might a computer-aided detection (CAD) system interfere with visual search? An eye-tracking study. Acad. Radiol. 19, 10 (2012), 1260-1267.
10. M. T. Dzindolet, S. A. Peterson, R. A. Pomranky, L. G. Pierce, and H. P. Beck. 2003. The role of trust in automation reliance. Int. J. Human- Comput. Studies 58, 6 (June 2003), 697-718.
11. T. W. Freer and M. J. Ulissey. 2001. Screening mammography with computer-aided detection: Prospective study of 12,860 patients in a community breast center 1. Radiology 220, 3 (2001), 781-786.
12. M. L. Giger, K. Doi, H. MacMahon, R. M. Nishikawa, K. R. Hoffmann, R. A. Vyborny, C. J. Schmidt, H. Jia, K. Abe, and X. Chen. 1993. An "intelligent" workstation for computer-aided diagnosis.Radiographics 13, 3 (1993), 647-656.
13. D. M. Green and J. A. Swets. 1966. Signal Detection Theory and Psychophysics. Wiley, NewYork.
14. T. Hengl and N. Toomanian. 2006. Maps are not what they seem: Representing uncertainty in soil-property maps. In Proceedings of the 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, M. Caetanoand M. Painho (Eds.). Instituto Geographico Portugues IGP, 805-813.
15. R. Hupse, M. Samulski, M. B. Lobbes, R. M. Mann, R. Mus, G. J. den Heeten, D. Beijerinck, R. M. Pijnappel, C. Boetes, and N. Karssemeijer. 2013. Computer-aided detection of masses at mammography: Interactive decision support versus prompts. Radiology 266, 1 (2013), 123-129.
16. M. Kaur, J. Kaur, and J. Kaur. 2011. Survey of contrast enhancement techniques based on histogram equalization. Int. J. Adv. Comput. Sci. Appl. 2, 7 (2011), 137-141.
17. R. T. Kneusel. 2016. Improving human-classifier interaction through enhanced highlighting techniques. Ph.D. Dissertation. Department of Computer Science, University of Colorado at Boulder.
18. E. A. Krupinski, C. F. Nodine, and H. L. Kundel. 1993. Perceptual enhancement of tumor targets in chest x-ray images. Percept. Psychophys. 53, 5 (1993), 519-526.
19. Y. LeCun, Y. Bengio, and G. E. Hinton. 2015. Deep learning. Nature 521 (2015), 436-444.
20. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. 1998. Gradient-based learning applied to document recognition. Proc. IEEE 86, 11 (1998), 2278-2324.
21. S.-K. Lee, C.-S. Lo, C.-M.Wang, P.-C. Chung, C.-I. Chang, C.-W. Yang, and P.-C. Hsu. 2000. A computer-aided design mammography screening system for detection and classification of microcalcifications. Int. J. Med. Informat. 60, 1 (2000), 29-57.
22. C. D. Lehman, R. D. Wellman, D. S. M. Buist, K. Kerlikowske, A. N. A. Tosteson, and D. L. Miglioretti. 2016. Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern. Med. 11 (2016), 1828-1837.
23. A. M. MacEachren, A. Robinson, S. Hopper, S. Gardner, R. Murray, M. Gahegan, and E. Hetzler. 2005. Visualizing geospatial information uncertainty: What we know and what we need to know. Cartogr. Geogr. Info. Sci. 32, 3 (2005), 139-160.
24. M. E. J. Masson and G. R. Loftus. 2003. Using confidence intervals for graphically based data interpretation. Can. J. Exp. Psychol./Revue canadienne de psychologie expérimentale 57, 3 (2003), 203.
25. M. C. Mozer. 1991. The Perception of Multiple Objects: A Connectionist Approach. MIT Press, Cambridge, MA.
26. R. Parasuraman and V. Riley. 1997. Humans and automation: Use, misuse, disuse, abuse. Human Factors 39, 2 (1997), 230-253.
27. L. E. Philpotts. 2009. Can computer-aided detection be detrimental to mammographic interpretation? 1. Radiology 253, 1 (2009), 17-22.
28. J. A. Richards and X. Jia. 1999. Remote Sensing Digital Image Analysis: An Introduction, 4th ed. Springer, Berlin.
29. B. Sahiner, H.-P. Chan, L. M. Hadjiiski, P. N. Cascade, E. A. Kazerooni, A. R. Chughtai, C. Poopat, T. Song, L. Frank, J. Stojanovska, and others. 2009. Effect of CAD on radiologists' detection of lung nodules on thoracic CT scans: Analysis of an observer performance study by nodule size. Acad. Radiol. 16, 12 (2009), 1518-1530.
30. M. Samulski, A. Hupse, C. Boetes, G. den Heeten, and N. Karssemeijer. 2009. Analysis of probed regions in an interactive CAD system for the detection of masses in mammograms. Proceedings of SPIE 7263 (2009), 726314-726314-9. DOI:http://dx.doi.org/10.1117/12.813391
31. M. Samulski, R. Hupse, C. Boetes, R. D. M. Mus, G. J. den Heeten, and N. Karssemeijer. 2010. Using computer-aided detection in mammography as a decision support. Eur. Radiol. 20, 10 (2010), 2323-2330.
32. J. Snoek, H. Larochelle, and R. P. Adams. 2012. Practical bayesian optimization of machine learning algorithms. In Advances in Neural Information Processing Systems 25, F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger (Eds.). Curran Associates, Inc., La Jolla, CA, 2951-2959.
33. A. M. Treisman and G. Gelade. 1980. A feature-integration theory of attention. Cogn. Psychol. 12, 1 (1980), 97-136.
34. F. J. M. Van derWel, L. C. Van der Gaag, and B. G. H. Gorte. 1998. Visual exploration of uncertainty in remote-sensing classification. Comput. Geosci. 24, 4 (1998), 335-343.
35. J. L. Van Genderen, B. F. Lock, and P. A. Vass. 1978. Remote sensing: Statistical testing of thematic map accuracy. Remote Sens. Environ. 7, 1 (1978), 3-14.
36. M. Yasmin, M. Sharif, S. Masood, M. Raza, and S. Mohsin. 2012. Brain image enhancement-A survey. World Appl. Sci. J. 17, 9 (2012), 1192-1204.
37. B. Zheng, M. A. Ganott, C. A. Britton, C. M. Hakim, L. A. Hardesty, T. S. Chang, H. E. Rockette, and D. Gur. 2001. Soft-copy mammographic readings with different computer-assisted detection cuing environments: Preliminary findings 1. Radiology 221, 3 (2001), 633-640.
38. B. Zheng, R. G. Swensson, S. Golla, C. M. Hakim, R. Shah, L. Wallace, and D. Gur. 2004. Detection and classification performance levels of mammographic masses under different computer-aided detection cueing environments 1. Acad. Radiol. 11, 4 (2004), 398-406.