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Grant Details

Grant Number: 5R01CA236791-02 Interpret this number
Primary Investigator: Kellman, Philip
Organization: University Of California Los Angeles
Project Title: Perceptual and Adaptive Learning in Cancer Image Interpretation
Fiscal Year: 2020


Project Summary/Abstract Cancer screening from visual displays, as in dermatology and radiology, depends crucially on the expertise of medical practitioners, but current data indicate that even among experienced professionals there are significant and persistent error rates. While there have been impressive advances in the technologies of medical imaging, considerably less attention has been paid to the learning processes involved in the training of medical image interpretation. Research in perception and cognition indicates that the central process by which people become able to detect and classify complex and subtle patterns and structures in visual images is a process known as perceptual learning. Through perceptual learning mechanisms, with appropriate practice in a given domain, the brain progressively improves information extraction to optimize task performance. These mechanisms are largely unaffected by the traditional didactic instruction common in medical education; instead, they depend on interaction with large numbers of examples with task-relevant feedback. Recent work has shown that application of principles of perceptual learning can dramatically accelerate accuracy and fluency in medical learning domains. Evidence suggests that these training methods can be markedly enhanced, and customized for individual learners, by incorporating novel adaptive learning algorithms based on principles of learning and memory. The primary aim of this project is to investigate principles and mechanisms of perceptual and adaptive learning in the learning of multiple diagnostic categories in dermatologic screening and mammography, with the ultimate aim of improving training and proficiency in cancer image interpretation. Studies with novices in lab settings will establish basic principles and hypotheses, and selective studies with nurse melanographers, residents, and physicians will test validation with actual practitioners. Culminating studies of melanographers in actual dermatologic screening settings will compare practitioners who train with best-practices perceptual- adaptive learning modules (PALMs) to control participants. Specific studies will investigate the incorporation of signal detection concepts into adaptive perceptual learning systems; the role of comparisons in defining and differentiating perceptual categories; the relative benefits of passive and active learning episodes across learning phases; and the relationship between the stringency of mastery criteria and the degree to which resulting performance is accurate, fluent, generalizable, and long-lasting.


Constant curvature segments as building blocks of 2D shape representation.
Authors: Baker N. , Garrigan P. , Kellman P.J. .
Source: Journal of experimental psychology. General, 2021 Aug; 150(8), p. 1556-1580.
EPub date: 2020-12-17.
PMID: 33332142
Related Citations

Mastering Electrocardiogram Interpretation Skills Through a Perceptual and Adaptive Learning Module.
Authors: Krasne S. , Stevens C.D. , Kellman P.J. , Niemann J.T. .
Source: AEM education and training, 2021 Apr; 5(2), p. e10454.
EPub date: 2020-05-05.
PMID: 33796803
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Constant curvature modeling of abstract shape representation.
Authors: Baker N. , Kellman P.J. .
Source: PloS one, 2021; 16(8), p. e0254719.
EPub date: 2021-08-02.
PMID: 34339436
Related Citations

Comparing Adaptive and Random Spacing Schedules during Learning to Mastery Criteria.
Authors: Mettler E. , Burke T. , Massey C.M. , Kellman P.J. .
Source: CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference, 2020 Jul-Aug; 2020, p. 773-779.
PMID: 34337609
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Local features and global shape information in object classification by deep convolutional neural networks.
Authors: Baker N. , Lu H. , Erlikhman G. , Kellman P.J. .
Source: Vision research, 2020 07; 172, p. 46-61.
EPub date: 2020-05-12.
PMID: 32413803
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