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Perceptual and Adaptive Learning in Cancer Image Interpretation

癌症图像解读中的感知和适应性学习

基本信息

批准号：
10464901
负责人：
PHILIP J KELLMAN
金额：
$ 48.12万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-18 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10464901
关键词：
Active Learning Area Attention Brain Categories Cessation of life Classification Cognition Competence Complex Custom Data Dermatologic Dermatology Detection Diagnostic Effectiveness Ensure Failure Feedback Frustration Goals Human Image Image Analysis Individual Information Retrieval Instruction Intuition Investigation Learning Learning Module Malignant Neoplasms Mammographic screening Mammography Measures Medical Medical Education Medical Imaging Medical Technology Memory Methods Neoplasms Nurses Paired Comparison Participant Pattern Perception Perceptual learning Performance Persons Phase Physicians Process Radiology Specialty Reaction Time Recurrence Research Research Project Grants Role Screening for cancer Signal Detection Analysis Signal Transduction Skin Cancer Structure System Task Performances Techniques Testing Training Validation Visual Work adaptive learning base cancer classification cancer imaging cancer therapy design economic cost experience hands-on learning human imaging imaging modality improved innovation learning algorithm learning outcome malignant breast neoplasm novel premature screening tool unnecessary treatment

项目摘要

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.

项目总结/摘要从视觉显示器上进行癌症筛查，如皮肤科和放射科，关键取决于以下方面的专业知识：医生，但目前的数据表明，即使在有经验的专业人员，和持续错误率。虽然医学成像技术已经取得了令人印象深刻的进步，在医学图像的训练中，对学习过程的关注相对较少解释。感知和认知研究表明，人们成为人的核心过程能够检测和分类视觉图像中复杂和微妙的图案和结构的过程称为知觉学习通过感知学习机制，在特定领域进行适当的练习，大脑逐步提高信息提取，以优化任务性能。这些机制是在很大程度上不受医学教育中常见的传统说教式教学的影响;相反，他们依赖于与大量具有任务相关反馈的示例进行交互。最近的研究表明，感知学习原理的应用可以大大提高医学语言的准确性和流畅性。学习领域。有证据表明，这些培训方法可以得到明显加强，并根据具体情况加以调整。对于个人学习者，通过结合基于学习原理的新的自适应学习算法，记忆本研究的主要目的是探讨知觉学习和适应性学习的原理和机制在学习皮肤病筛查和乳房X光检查中的多种诊断类别，最终目的是提高癌症图像解读的培训和熟练程度。实验室中的新手研究设置将建立基本原则和假设，并与护士黑色素分析师选择性研究，住院医师和医生将与实际从业者进行验证。黑色素分析师的研究达到顶峰，实际的皮肤病筛查设置将比较与最佳实践知觉训练的从业者，自适应学习模块（PALMs）控制参与者。具体的研究将调查纳入信号检测概念到自适应感知学习系统;比较的作用，区分感知类别;被动和主动学习事件的相对好处，学习阶段;以及掌握标准的严格性与所得到的性能是准确的、流畅的、可推广的和持久的。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Connecting Adaptive Perceptual Learning and Signal Detection Theory in Skin Cancer Screening.

将自适应感知学习和信号检测理论连接到皮肤癌筛查中。

DOI：
发表时间：
2023
期刊：
CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference
影响因子：
0
作者：
Kellman,PhilipJ;Krasne,Sally;Massey,ChristineM;Mettler,EverettW
通讯作者：
Mettler,EverettW

Multiple expressions of "expert" abnormality gist in novices following perceptual learning.

DOI：
10.1186/s41235-023-00462-5
发表时间：
2023-02-01
期刊：
COGNITIVE RESEARCH-PRINCIPLES AND IMPLICATIONS
影响因子：
4.1
作者：
DiGirolamo, Gregory J.;DiDominica, Megan;Qadri, Muhammad A. J.;Kellman, Philip J.;Krasne, Sally;Massey, Christine;Rosen, Max P.
通讯作者：
Rosen, Max P.

Evaluating the Use of Supplemental Training Technologies in Dermatology Education.

评估补充培训技术在皮肤病学教育中的使用。

DOI：
发表时间：
2021
期刊：
Journal of dermatology for physician assistants : Official journal of the Society of Dermatology Physician Assistants
影响因子：
0
作者：
Aycock,MalloryM;Marker,CraigD;Kellman,PhilipJ
通讯作者：
Kellman,PhilipJ

Comparisons in Adaptive Perceptual Category Learning.

自适应感知类别学习的比较。

DOI：
发表时间：
2022
期刊：
CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference
影响因子：
0
作者：
Jacoby,VictoriaL;Massey,ChristineM;Mettler,Everett;Kellman,PhilipJ
通讯作者：
Kellman,PhilipJ

Mastering Electrocardiogram Interpretation Skills Through a Perceptual and Adaptive Learning Module