Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-02T21:55:04.978Z Has data issue: false hasContentIssue false

11 - Acquiring Expertise in Radiologic Image Interpretation

from Part II - Science of Image Perception

Published online by Cambridge University Press:  20 December 2018

By
Calvin F. Nodine  and
Claudia Mello-Thoms
Edited by
Ehsan Samei  and
Elizabeth A. Krupinski
Ehsan Samei
Affiliation:
Duke University Medical Center, Durham
Elizabeth A. Krupinski
Affiliation:
Emory University, Atlanta
Get access

Summary

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
The Handbook of Medical Image Perception and Techniques , pp. 167 - 187
Publisher: Cambridge University Press
Print publication year: 2018

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ahissar, M., Hochstein, S. (2004). The reverse hierarchy theory of visual perceptual learning. Trends Cogn Sci, 8, 457–464.CrossRefGoogle ScholarPubMed
Anderson, J.R. (1995). Cognitive Psychology and its Implications, 4th ed. New York: W.H. Freeman.Google Scholar
Antes, J.R., Penland, J.G. (1981). Picture context effects on eye movement patterns. In: Fisher, D.F., Monty, R.A., Senders, J.W. (eds.) Eye Movements: Cognition and Visual Perception. Hillsdale, NJ: Lawrence Erlbaum, pp. 157–170.Google Scholar
Baghdady, M., Carnahan, H., Lam, E.W.N., Woods, N.N. (2014). Dental and dental hygiene students diagnostic accuracy in oral radiology: effect of diagnostic strategy and instructional method. J Dent Educ, 78, 1279–1285.Google Scholar
Bar, M., Kassam, K.S., Ghuman, A.S., et al. (2006). Top-down facilitation of visual recognition. Proc Natl Acad Sci, 103, 449–454.Google Scholar
Barlow, W.E., Chi, C., Carney, P.A., et al. (2004). Accuracy of screening mammography interpretation by characteristics of radiologists. J Natl Cancer Inst, 96, 1840–1850.Google Scholar
Bass, J.C., Chiles, C. (1990). Visual skill: correlation with detection of solitary pulmonary nodules. Invest Radiol, 25, 994–998.Google Scholar
Beam, C.A., Conant, E.F., Sickles, E.A. (2003). Association of volume and volume-independent factors with accuracy in screening mammogram interpretation. J Natl Cancer Inst, 95, 282–290.CrossRefGoogle ScholarPubMed
Berbaum, K.S., Franken, E.A. Jr., Dorfman, D.D., et al. (1990). Satisfaction of search in diagnostic radiology. Invest Radiol, 25, 133–140.Google Scholar
Berlin, L. (2003). Malpractices issues in radiology – breast cancer, mammography, and malpractice litigation: the controversies continue. Am J Roentgenol, 180, 1229–1237.Google Scholar
Beutel, J., Van Metter, R., Kundel, H (eds.) (2001). Handbook of Medical Imaging. Vol. 1: Physics and Psychophysics. Bellingham, WA: SPIE Press.Google Scholar
Brennan, P.C., Trieu, P.D., Tapia, K., Ryan, J., Mello-Thoms, C., Lee, W. (2014) BREAST: a novel strategy to improve the detection of breast cancer. Proc 12th Int Workshop on Breast Imaging. Springer LNCS: 8539.Google Scholar
Carpenter, G.A., Grossberg, S. (1993). Normal and amnesic learning: recognition memory by a neural model of cortico-hyppocampal interactions. Trends Neurosci, 16, 131–137.Google Scholar
Carpenter, G.A., Grossberg, S., Lesher, G.W. (1998). The what-and-where filter. A spatial mapping neural network for object recognition and image understanding. Comp Vis Image Underst, 69, 1–22.CrossRefGoogle Scholar
Cave, K.R., Batty, M.J. (2006). From searching for features to searching for threat: drawing the boundary between preattentive and attentive vision. Vis Cogn, 14, 629–646.Google Scholar
Charlin, B. (1998). Script questionnaires: their use for assessing diagnostic knowledge in radiology. Med Teach, 20, 567–571.Google Scholar
Charness, N., Krampe, R., Mayr, U. (1996). The role of practice and coaching in entrepreneurial skill domains: an international comparison of life-span chess skill acquisition. In: Ericsson, K.A. (ed.) The Road to Excellence. Mahwah, NJ: Erlbaum, pp. 51–80.Google Scholar
Charness, N., Reingold, E.M., Pomplun, M., et al. (2001). The perceptual aspects of skilled performance in chess: evidence from eye movements. Mem Cognit, 29, 1146–1152.Google Scholar
Chase, W.G., Simon, H. (1973a). The mind’s eye in chess. In: Chase, W.G. (ed.) Visual Information Processing. New York, NY: Academic Press, pp. 215–281.Google Scholar
Chase, W.G., Simon, H.A. (1973b). Perception in chess. Cognit Psychol, 4, 86–112.Google Scholar
Chi, M.T.H., Glaser, R., Rees, E. (1982). Expertise in problem solving. In: Sternberg, R. (ed.). Advances in the Psychology of Human Intelligence. Hillsdale, NJ: Lawrence Erlbaum, pp. 17–76.Google Scholar
Christensen, E.E., Murry, R.C., Holland, K., et al. (1981). The effect of search time on perception. Radiology, 138, 361–365.CrossRefGoogle ScholarPubMed
Croskerry, P. (2009a). A universal model of diagnostic reasoning. Acad Med, 84, 1222–1228.Google Scholar
Croskerry, P. (2009b). Clinical cognition and diagnostic error: applications of a dual processing model of reasoning. Adv Health Sci Educ, 14, 27–35.Google Scholar
Croskerry, P., Petrie, D.A., Reilly, J.B., Tait, G. (2014). Deciding about fast and slow decisions. Acad Med, 89, 197–200.Google Scholar
Crowley, R.S., Naus, G.J., Steward, J. III, Friedman, C.P. (2003). Development of visual diagnostic expertise in pathology: an information-processing study. J Am Med Inform Assoc, 10, 39–51.Google Scholar
Custers, E.J.F.M. (2015). Thirty years of illness scripts: theoretical origins and practical applications. Med Teach, 37, 457–462.Google Scholar
de Groot, A.D. (1978). Thought and Choice in Chess. The Hague, The Netherlands: Mouton.Google Scholar
de Groot, A.D., Gobet, F. (1996). Perception and Memory in Chess. Assen, The Netherlands: Van Gorum.Google Scholar
Donovan, T., Litchfield, D. (2013). Looking for cancer: expertise related differences in searching and decision making. Appl Cognit Psychol, 27, 43–49.CrossRefGoogle Scholar
Drew, T., Vo, M.L.H., Olwal, A., Jacobson, F., Seltzer, S.E., Wolfe, J.M. (2013). Scanners and drillers: characterizing expert visual search through volumetric images. J Vision, 13, 1–13.Google Scholar
Dreyfus, H.L., Dreyfus, S.E. (1986). Mind Over Machine: The Power of Human Intuition and Expertise in the Era of the Computer. New York, NY: The Free Press.Google Scholar
Dulaney, C.L., Marks, W. (2007). The effects of training and transfer on global/local processing. Acta Psychol, 125, 203–220.Google Scholar
Ericsson, K.A. (1996). The acquisition of expert performance. In: Ericsson, K.A. (ed.) The Road to Excellence. Mahwah, NJ: Lawrence Erlbaum, pp. 1–50.Google Scholar
Ericsson, K.A., Kintsch, W. (1995). Long term working memory. Psychol Rev, 102, 211–245.Google Scholar
Ericsson, K.A., Charness, N., Feltovich, P.J., et al. (2006). The Cambridge Handbook of Expertise and Expert Performance. New York, NY: Cambridge University Press.Google Scholar
Esserman, L., Cowley, H., Carey, E., et al. (2002). Improving accuracy of mammography: volume and outcome relationships. J Natl Cancer Inst, 94, 369–375.Google Scholar
Estes, W.K. (1994). Classification and Cognition. New York, NY: Oxford University Press.Google Scholar
Evans, K.K., Haygood, T.M., Cooper, J., Culpan, A.-M., Wolfe, J.M. (2016). A half-second glimpse often lets radiologists identify breast cancer cases when viewing the mammogram of the opposite breast. Proc Natl Acad Sci, 113, 10292–10297.Google Scholar
Evered, A., Walker, D., Watt, A.A., Perham, N. (2014). Untutored discrimination training on paired cell images influences visual learning in cytopathology. Cancer Cytopathol, 122, 200–210.Google Scholar
Feltovitch, P.J., Barrows, H.S. (1984). Issues of generality in medical problem solving. In: Schmidt, H.G., de Volder, M.L. (eds.) Tutorials in Problem Based Learning. Assen/Maastrich, The Nertherlands: Van Gorcum, pp. 128–142.Google Scholar
Fenton, J.J., Taplin, S.H., Carney, P.A., et al. (2007). Influence of computer-aided detection on performance of screening mammography. N Engl J Med, 356, 1399–1409.Google Scholar
Fink, G.R., Halligan, P.W., Marshall, J.C., et al. (1996). Where in the brain does visual attention select the forest and the trees? Nature, 382, 626–628.Google Scholar
Gale, A.G., Walker, G.E. (1991). Design for performance: quality assessment in a national breast screening programme. In: Lovesay, E.J. (ed.) Ergonomics-Design for Performance. London, England: Taylor & Francis.Google Scholar
Gale, A.G., Vernon, J., Millar, K., Worthington, B.S. (1983). Interpreting radiographs in a single glance. Radiology, 149, 253.Google Scholar
Gibson, E.J. (1969). Principles of Perceptual Learning. New York, NY: Appleton-Century-Crofts.Google Scholar
Gibson, J.J. (1979). The Ecological Approach to Visual Perception. Boston, MA: Houghton Mifflin.Google Scholar
Gibson, J.J., Gibson, E.J. (1955). Perceptual learning: differentiation or enrichment? Psychol Rev, 62, 32–41.Google Scholar
Gobet, F. (2016). Understanding Expertise: A Multi-Disciplinary Approach. London, UK: Palgrave.Google Scholar
Goldstone, R.L., de Leeuw, J.R., Landy, D.H. (2015). Fitting perception in and to cognition. Cognition, 135, 24–29.Google Scholar
Gordon, S.E. (2016). Implications of cognitive theory for knowledge acquisition. In: Hoffman, R.R. (ed.) The Psychology of Expertise: Cognitive Research and Empirical AI. New York, NY: Routledge, pp. 99–120.Google Scholar
Gregory, R.L. (1970). The Intelligent Eye. New York, NY: McGraw-Hill.Google Scholar
Gregory, R.L. (2001). Analog or digital? In: Parks, T.E. (ed.) Looking at Looking. Thousand Oaks, CA: Sage, pp. 115–118.Google Scholar
Gregory, R.L., Gombrich, E.H. (1973). The confounded eye. In: Gregory, R.L. (ed.) Illusion in Nature and Art. London, England: Duckworth, pp. 49–95.Google Scholar
Grill-Spector, K., Kanwisher, N. (2005). Visual recognition: as soon as you know it is there, you know what it is. Psychol Sci, 16, 152–160.Google Scholar
Gunderman, R.B. (2000). Illuminating the “black boxes” of learning and recall. Acad Radiol, 7, 641–646.Google Scholar
Gunderman, R.B. (2001). Is technical school a good model for radiology residency? Am J Roentgenol, 177, 1005–1007.Google Scholar
Gunderman, R.B., Williamson, K.B., Frank, M., et al. (2003). Learner-centered education. Radiology, 227, 15–17.Google Scholar
Gur, D., Sumkin, J.H., Rockette, H.E., et al. (2004). Changes in breast cancer detection and mammography recall rates after the introduction of a computer-aided detection system. J Natl Cancer Inst, 96,185–190.Google Scholar
Haider, H., Frensch, P.A. (1996). The role of information reduction in skill acquisition. Cogn Psychol, 30, 304–337.CrossRefGoogle ScholarPubMed
Haider, H., Frensch, P.A. (1999). Eye movement during skill acquisition: more evidence for the information reduction hypothesis. J Exp Psychol: Learn Mem Cogn, 25, 172–190.Google Scholar
Haller, S., Radue, E.W. (2005). What is different about a radiologist’s brain? Radiology, 236, 983–989.Google Scholar
Harries, C., Evans, J.S.B.T., Dennis, I. (2000). Measuring doctors’ self-insight into their treatment decisions. Appl Cognit Psychol, 14, 455–477.Google Scholar
Herzog, M.H., Cretenoud, A.F., Grzeczkowski, L. (2017). What is new in perceptual learning? J Vision, 17, 23.Google Scholar
Hmelo-Silver, C.E., Pfeffer, M.G. (2004). Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions. Cogn Sci, 28, 127–138.CrossRefGoogle Scholar
Hochberg, J.E. (1978) Perception, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Jacobs, R.A. (2010) Integrated approaches to perceptual learning. Topics Cogn Sci, 2, 182–188.Google Scholar
Johnson, A., Proctor, R.W. (2017). Skill Acquisition and Training: Achieving Expertise in Simple and Complex Tasks. New York, NY: Routledge.Google Scholar
Joseph, G-M., Patel, V.L. (1990). Domain knowledge and hypothesis generation in diagnostic reasoning. Med Dec Mak, 10, 31–46.Google Scholar
Kahneman, D. (2011). Thinking Fast and Slow. New York, NY: Farra, Straus and Giroux.Google Scholar
Kalyuga, S., Ayres, P., Chandler, P., Sweller, J. (2003). The expertise reversal effect. Educ Psychol, 38, 23–31.Google Scholar
Kellman, P.J. (2002) Perceptual learning. In: Gallistel, R. (ed.) Stevens’ Handbook of Experimental Psychology, Vol. 3, Learning, Motivation and Emotion. New York, NY: Wiley, pp. 259–299.Google Scholar
Kellman, P.J. (2013). Adaptive and perceptual learning technologies in medical education and training. Milit Med, 178, 98–106.Google Scholar
Kellman, P.J., Massey, C.M., Son, J. (2010). Perceptual learning modules in mathematics: enhancing students’ pattern recognition, structure extraction, and fluency. Topics Cogn Sci, 2, 285–305.Google Scholar
Kok, E.M., de Bruin, A.B.H., Robben, S.G.F., van Merrienboer, J.J.G. (2012). Looking in the same manner but seeing it differently: bottom-up and expertise effects in radiology. Appl Cognit Psychol, 26, 854–862.Google Scholar
Kok, E.M., van Geel, K., van Merrienboer, J.J.G., Robben, S.G.F. (2017). What we do and do not know about teaching medical image interpretation. Frontiers Psychol, 8, 309.Google Scholar
Krupinski, E.A. (1996). Visual scanning patterns of radiologists searching mammograms. Acad Radiol, 3, 137–144.Google Scholar
Kuhn, T. (1962). The structure of scientific revolutions. Int Encycloped Unif Sci, 2(2).Google Scholar
Kulatunga-Moruzi, C., Brooks, L.R., Norman, G.R. (2004). Using comprehensive feature lists to bias medical diagnosis. J Exp Psychol: Learn Mem Cog, 30, 563–572.Google Scholar
Kundel, H.L. (2007). How to minimize perceptual error and maximize expertise in medical imaging. Proc SPIE Med Imag: Image Perc, Obs Perf Tech Assess, 6515, 651508-1–651508-11.Google Scholar
Kundel, H.L., LaFollette, P.S. (1972). Visual search patterns and experience with radiological images. Radiology, 103, 523–528.Google Scholar
Kundel, H.L., Nodine, C.F. (1975). Interpreting chess radiographs without visual search. Radiology, 108, 527–532.Google Scholar
Kundel, H.L., Wright, D.J. (1969). The influence of prior knowledge on visual search strategies during viewing of chest radiographs. Radiology, 2, 315–320.Google Scholar
Kundel, H.L., Nodine, C.F., Carmody, D.P. (1978). Visual scanning, pattern recognition and decision-making in pulmonary nodule detection. Invest Radiol, 13, 175–181.Google Scholar
Kundel, H.L., Nodine, C.F., Conant, E.F., Weinstein, S.P. (2007). Holistic component of image perception in mammogram interpretation: gaze-tracking study. Radiology, 242, 396–402.Google Scholar
Kundel, H.L., Nodine, C.F., Krupinski, E.A., Mello-Thoms, C. (2008). Using gaze-tracking data and mixture distribution analysis to support a holistic model for the detection of cancers on mammograms. Acad Radiol, 15, 881–886.Google Scholar
Lesgold, A., Rubinson, H., Feltovich, P., et al. (1988). Expertise in a complex skill: diagnosing X-ray pictures. In: Chi, M., Glaser, R., Farr, M. (eds.) The Nature of Expertise. Hillsdale, NJ: Erlbaum, pp. 311–342.Google Scholar
Llewellyn-Thomas, E., Lansdown, E.L. (1963). Visual search patterns of radiologists in training. Radiology, 1, 288–292.Google Scholar
Lu, Z-L., Lin, Z., Dosher, B.A. (2016). Translating perceptual learning from the laboratory to applications. Trends Cogn Sci, 20, 561–563.Google Scholar
Mackworth, N.H., Morandi, A.J. (1967). The gaze selects informative details within pictures. Perc Psychophys, 2, 547–552.Google Scholar
Mello-Thoms, C., Dunn, S., Nodine, C.F., Kundel, H.L., Weinstein, S.P. (2002). The perception of breast cancer: what differentiates missed from reported cancers in mammography? Acad Radiol, 9, 1004–1012.Google Scholar
Mello-Thoms, C., Hardesty, L., Sumkin, J., et al. (2005). Effects of lesion conspicuity on visual search in mammogram reading. Acad Radiol, 12, 830–840.Google Scholar
Mello-Thoms, C., Ganott, M., Sumkin, J., et al. (2008). Different search patterns and similar decision outcomes: how can experts agree in the decisions they make when reading digital mammograms? In: Krupinski, E.A. (ed.) Int Work Digital Mammo, Lecture Notes on Computer Science 5116. Berlin, Germany: Springer-Verlag, pp. 212–219.Google Scholar
Miller, G.A. (1956). The magical number seven, plus or minus two: some limits to our capacity for processing information. Psychol Rev, 63, 81–97.Google Scholar
Monsky, W.L., Levine, D., Mehta, T.S., et al. (2002). Using a sonographic simulator to assess residents before overnight call. Am J Roentgenol, 178, 35–39.Google Scholar
Moravec, H. (1988). Mind Children: The Future of Robot and Human Intelligence. Cambridge, MA: Harvard University Press.Google Scholar
Mueller, D., Georges, A., Vashow, D. (2007). Cooperative learning as applied to resident instruction in radiology reporting. Acad Radiol, 14, 1577–1583.Google Scholar
Myles-Worsley, M., Johnston, W.A., Simons, M.A. (1988). The influence of expertise on X-ray image processing. J Exp Psychol, 14, 553–557.Google Scholar
Navon, D. (1977). Forest before trees. The precedence of global features in visual perception. Cogn Psychol, 9, 353–383.Google Scholar
Neisser, U. (1967). Cognitive Psychology. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Neisser, U. (1976). Cognition and Reality. San Francisco, CA: W.H. Freeman.Google Scholar
Newell, A., Simon, H. (1972). Human Problem Solving. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
Nodine, C.F., Krupinski, E.A. (1998). Perceptual skill, radiology expertise, and visual test performance with NINA and WALDO. Acad Radiol, 5, 603–612.Google Scholar
Nodine, C.F., Mello-Thoms, C. (2000). The nature of expertise in radiology. In: Beutel, J., Kundel, H.L., Van Metter, R.L. (eds.) Handbook of Medical Imaging, Volume 1: Physics and Psychophysics. Bellingham, WA: SPIE Press, pp. 859–893.Google Scholar
Nodine, C.F., Kundel, H.L., Mello-Thoms, C., et al. (1999). How experience and training influence mammography expertise. Acad Radiol, 1, 575–585.Google Scholar
Nodine, C.F., Mello-Thoms, C., Kundel, H.L., et al. (2001). Blinded review of retrospectively visible unreported breast cancers: an eye-position analysis. Radiology, 221, 122–129.Google Scholar
Nodine, C.F., Mello-Thoms, C., Kundel, H.L., Weinstein, S.P. (2002). Time course of perception and decision making during mammographic interpretation. Am J Roentgenol, 179, 917–923.Google Scholar
Norman, G.R., Brooks, L.R., Colle, C.L., Hatala, R.M. (2000). The benefit of diagnostic hypotheses in clinical reasoning: experimental study of an instructional intervention for forward and backward reasoning. Cogn Instruct, 17, 433–448.Google Scholar
Norman, G., Eva, K., Brooks, L., Hamstra, S. (2006). Expertise in medicine and surgery. In: Ericsson, K.A., Charness, N., Feltovich, P.J., Hoffman, R.R. (eds.) The Cambridge Handbook of Expertise and Expert Performance. New York, NY: Cambridge University Press, pp. 339–353.Google Scholar
Norman, G., Sherbino, J., Dore, K., et al. (2014). The etiology of diagnostic errors: a controlled trial of system 1 versus system 2 reasoning. Acad Med, 89, 277–284.Google Scholar
Olshausen, B.A., Field, D.J. (2000). Vision and the coding of natural images. Am Scientist, 88, 238–245.Google Scholar
Peterson, M.A., Rhodes, G. (2003). Perception of Faces, Objects and Scenes: Analytic and Holistic Processes. New York, NY: Oxford University Press.Google Scholar
Pisano, E.D., Gatsonis, C., Hendrick, E., et al. (2005). Diagnostic performance of digital versus film mammography for breast-cancer screening. N Engl J Med, 353, 1773–1783.Google Scholar
Puri, A.M., Wojciulik, E. (2008). Expectation both helps and hinders object perception. Vision Res, 48, 589–597.Google Scholar
Rackow, P.L., Spitzer, V.M., Hendee, W.R. (1987). Detection of low-contrast signals: a comparison of observers with and without radiology training. Invest Radiol, 22, 311–314.Google Scholar
Raufaste, E., Eyrolle, H., Marine, C. (1998). Pertinence generation in radiological diagnosis: spreading activation and the nature of expertise. Cogn Sci, 22, 517–546.Google Scholar
Ross, P.E. (2006). The expert mind. Scientific Am, 295, 64–71.Google Scholar
Sagi, D. (2011). Perceptual learning in vision research. Vision Res, 51, 1552–1566.CrossRefGoogle ScholarPubMed
Schmidt, H.G., Rikers, R.M.J. (2007). How expertise develops in medicine: knowledge encapsulation and illness script formation. Med Educ, 41, 1133–1139.Google Scholar
Siamecki, N., Graf, P. (1978). The generation effect: delineation of a phenomenon. J Exp Psychol: Human Learn Mem, 4, 592–604.Google Scholar
Smith-Bindman, R., Chu, P., Migloretti, D.L., et al. (2005). Physician predictors of mammographic accuracy. J Natl Cancer Inst, 97, 358–367.Google Scholar
Sowden, P.T., Davies, I.R.L., Roling, P. (2000). Perceptual learning of the detection of features in X-ray images: a functional role for improvement in adults’ visual sensitivity? J Exp Psychol: Human Percep Perf, 26, 379–390.Google Scholar
Swensson, R.G., Hessel, S.J., Herman, P.G. (1982). Radiographic interpretation with and without search: visual search aids the recognition of chess pathology. Invest Radiol, 17, 145–151.Google Scholar
Taylor, P.M. (2007). A review of research into the development of radiologic expertise: implications for computer-based training. Acad Radiol, 14, 1252–1263.Google Scholar
Tuddenham, W.J., Calvert, W.P. (1961). Visual search patterns in roentgen diagnosis. Radiology, 76, 255–256.Google Scholar
van der Gijp, A., van der Schaaf, M.F., van der Schaaf, I.C., Huige, J.C.B.M., Ravesloot, C.J., Schaik, J.P.J., ten Cate, T.J. (2014). Interpretation of radiological images: towards a framework of knowledge and skills. Adv Health Sci Educ, 19, 565–580.Google Scholar
van der Gijp, A., Ravesloot, C.J., van der Schaaf, M.F., et al. (2015). Volumetric and two-dimensional image interpretation show different cognitive processes in learners. Acad Radiol, 22, 632–639.Google Scholar
van der Gijp, A., Webb, E.M., Naeger, D.M. (2017). How radiologists think: understanding fast and slow thought processing and how it can improve our teaching. Acad Radiol, 24, 768–771.Google Scholar
Venjakob, A.C., Mello-Thoms, C. (2016). Review of prospects and challenges of eye tracking in volumetric imaging. J Med Imag, 3, 011002.Google Scholar
Venjakob, A.C., Marnitz, T., Phillips, P., Mello-Thoms, C. (2016). Image size influences visual search and perception of hemorrhages when reading cranial CT: an eye tracking study. Hum Factors, 58, 441–451.Google Scholar
Wen, G., Aizenman, A., Drew, T., Wolfe, J.M., Haygood, T.M., Markey, M.K. (2016). Computational assessment of visual search strategies in volumetric medical images. J Med Imag, 3, 015501.Google Scholar
Wise, J.A., Kubose, T., Chang, N., Russell, A., Kellman, P.J. (2000). Perceptual learning modules and mathematics and science instruction. In: Hoffman, P., Lemke, D. (eds.) Teaching and Learning in a Network World: TechEd 2000 Proceedings. Amsterdam, The Netherlands: IOS Press, pp. 169–176.Google Scholar
Wolfe, J.M., Cave, K.R., Franzel, S.L. (1989). Guided search: an alternative to the feature integration model for visual search. J Exp Psychol: Human Percep Perf, 15, 419–433.Google Scholar
Wood, B.P. (1999) Visual expertise. Radiology, 211, 1–3.Google Scholar
Wood, G., Knapp, K.M., Rock, B., Cousens, C., Roobottom, C., Wilson, M.R. (2013). Visual expertise in detecting and diagnosing skeletal fractures. Skelet Radiol, 42, 165–172.Google Scholar
Yang, G-Z., Dempere-Marco, L., Xiao-Peng, H., et al. (2002). Visual search: psychophysical models and practical applications. Image Vision Comp, 20, 291–305.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×