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31 - Perception and Training

from Part VI - Applied Perception

Published online by Cambridge University Press:  20 December 2018

Ehsan Samei
Affiliation:
Duke University Medical Center, Durham
Elizabeth A. Krupinski
Affiliation:
Emory University, Atlanta
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Publisher: Cambridge University Press
Print publication year: 2018

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References

ACR. (2017). What is a radiologist? Available at: www.acr.org/Quality-Safety/Radiology-Safety/Patient-Resources/About-Radiology (accessed March 29, 2017).Google Scholar
Allerton, D.J. (2010). The impact of flight simulation in aerospace. Aeronaut J, 114(1162), 747756.Google Scholar
Auffermann, W.F., Henry, T.S., Little, B.P., Tigges, S., Tridandapani, S. (2015). Simulation for teaching and assessment of nodule perception on chest radiography in nonradiology health care trainees. J Am Coll Radiol, 12(11), 12151222.Google Scholar
Auffermann, W.F., Little, B.P., Tridandapani, S. (2016). Teaching search patterns to medical trainees in an educational laboratory to improve perception of pulmonary nodules. J Med Imag, 3(1), 011006.Google Scholar
Baker, J.A., Kornguth, P.J., Floyd, C.E., Jr (1996). Breast imaging reporting and data system standardized mammography lexicon: observer variability in lesion description. AJR. Am J Roentgenol, 166(4), 773778.Google Scholar
Bjork, R.A. (1994). Memory and metamemory considerations in the training of human beings. In: Metcalfe, J., Shimamura, A. (eds.) Metacognition: Knowing about Knowing. Cambridge, MA: MIT Press.Google Scholar
Boeing., (2017). Statistical Summary of Commercial Jet Airplane Accidents, 1959–2016. Aviation Safety. Seattle, WA: Boeing Commercial Airplanes.Google Scholar
Bradley, A.P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn, 30(7), 11451159.Google Scholar
Brusilovsky, P., Millán, E. (2007). User models for adaptive hypermedia and adaptive educational systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. Lecture Notes in Computer Science, vol. 4321. Berlin: Springer-Verlag, pp. 3–53.Google Scholar
Buckle, C.E., Udawatta, V., Straus, C.M. (2013). Now you see it, now you don’t: visual illusions in radiology. Radiographics, 33(7), 20872102.Google Scholar
Chasen, M.H. (2001). Practical applications of Mach band theory in thoracic analysis. Radiology, 219(3), 596610.Google Scholar
Desser, T.S. (2007). Simulation-based training: the next revolution in radiology education? J Am Coll Radiol, 4(11), 816824.Google Scholar
Dictionary.com. (2017). Intern–2. Available at: www.dictionary.com/browse/intern.Google Scholar
Franklin, B. (2017). Benjamin Franklin quotes. Available at: www.brainyquote.com/quotes/quotes/b/benjaminfr383997.html.Google Scholar
Goodman, L. (2014). Felson's Principles of Chest Roentgenology. Philadelphia, PA: Saunders.Google Scholar
Grimm, L.J., Ghate, S.V., Yoon, S.C., Kuzmiak, C.M., Kim, C., Mazurowski, M.A. (2014a). Predicting error in detecting mammographic masses among radiology trainees using statistical models based on BI-RADS features. Med Phys, 41(3), 31909-31909.Google Scholar
Grimm, L.J., Kuzmiak, C.M., Ghate, S.V., Yoon, S.C., Mazurowski, M.A. (2014b). Radiology resident mammography training: interpretation difficulty and error-making patterns. Acad Radiol, 21(7), 888892.Google Scholar
Grimm, L.J., Zhang, J., Lo, J.Y., Johnson, K., Ghate, S.V., Mazurowski, M.A. (2016). Radiology trainee performance in digital breast tomosynthesis: relationship between difficulty and error making patterns. J Am Coll Radiol, 13(2), 198202.Google Scholar
Kundel, H.L., La Follette, P.S. (1972). Visual search patterns and experience with radiological images. Radiology, 103(3), 523528.Google Scholar
Kundel, H.L., Nodine, C.F., Carmody, D. (1978). Visual scanning, pattern recognition and decision-making in pulmonary nodule detection. Invest Radiol, 13(3), 175181.Google Scholar
Latham, G.P., Seijts, G., Crim, D. (2008). The effects of learning goal difficulty level and cognitive ability on performance. Can J Behav Sci/Rev Can Sci Comport, 40(4), 220.Google Scholar
Manning, D.J., Ethell, S.C., Donovan, T. (2004). Detection or decision errors? Missed lung cancer from the posteroanterior chest radiograph. Br J Radiol, 77(915), 231235.Google Scholar
Mazurowski, M.A., Tourassi, G.D. (2011). Exploring the potential of collaborative filtering for user-adaptive mammography education. In: Proceedings of the 2011 Biomedical Sciences and Engineering Conference: Image Informatics and Analytics in Biomedicine. IEEE.Google Scholar
Mazurowski, M.A., Baker, J.A., Barnhart, H.X., Tourassi, G.D. (2010). Individualized computer-aided education in mammography based on user modeling: concept and preliminary experiments. Med Phys, 37(3), 11521160.Google Scholar
Mazurowski, M.A., Barnhart, H.X., Baker, J.A., Tourassi, G.D. (2012). Identifying error-making patterns in assessment of mammographic BI-RADS descriptors among radiology residents using statistical pattern recognition. Acad Radiol, 19(7), 865871.Google Scholar
Meng, K., Lipson, J.A. (2011). Utilizing a PACS-integrated ultrasound-guided breast biopsy simulation exercise to reinforce the ACR practice guideline for ultrasound-guided percutaneous breast interventional procedures during radiology residency. Acad Radiol, 18(10), 13241328.Google Scholar
Rich, E. (1983). Users are individuals: individualizing user models. Int J Man-Machine Stud, 18(3), 199214.Google Scholar
Samia, H., Khan, S., Lawrence, J., Delaney, C.P. (2013). Simulation and its role in training. Clin Colon Rect Surg, 26(1), 4755.Google Scholar
Sarwani, N., Tappouni, R., Flemming, D. (2012). Use of a simulation laboratory to train radiology residents in the management of acute radiologic emergencies. AJR. Am J Roentgenol, 199(2), 244251.Google Scholar
SIMulation, . (2017). Emergent/critical care imaging SIMulation (SIM). Available at: http://widi.xray.ufl.edu/acr-resident-sim/history/ (accessed September 9, 2017).Google Scholar
Su, X., Khoshgoftaar, T.M. (2009). A survey of collaborative filtering techniques. Adv Artif Intell, 2009, 4.Google Scholar
Towbin, A.J., Paterson, B.E., Chang, P.J. (2008). Computer-based simulator for radiology: an educational tool. Radiographics, 28(1), 309316.Google Scholar
Wang, M., Wang, M., Grimm, L.J., Mazurowski, M.A. (2016a). A computer vision-based algorithm to predict false positive errors in radiology trainees when interpreting digital breast tomosynthesis cases. Exp Syst Applic, 64: 490–499.Google Scholar
Wang, M., Zhang, J., Grimm, L.J., Ghate, S.V. Walsh, R., Johnson, K.S., Lo, J.Y., Mazurowski, M.A. (2016b). Predicting false negative errors in digital breast tomosynthesis among radiology trainees using a computer vision-based approach. Exp Syst Applic, 56, 1–8.Google Scholar
Webb, G.I., Pazzani, M.J., Billsus, D. (2001). Machine learning for user modeling. User Model User-Adapt Interact, 11(1–2), 1929.Google Scholar
Zhang, J., Lo, J.Y., Kuzmiak, C.M., Ghate, S.V., Yoon, S.C., Mazurowski, M.A. (2014). Using computer-extracted image features for modeling of error-making patterns in detection of mammographic masses among radiology residents. Med Phys, 41(9), 91907.Google Scholar
Zhang, J., Silber, J.I., Mazurowski, M.A. (2015). Modeling false positive error making patterns in radiology trainees for improved mammography education. J Biomed Inform, 54, 50–57.Google Scholar

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