Roads Brett D, Xu Buyun, Robinson June K, Tanaka James W
Department of Compute Science, University of Colorado Boulder, 1111 Engineering Drive, ECOT 717, 430 UCB, Boulder, CO, 80309-0430, USA.
Department of Psychology, University of Victoria, P. O. Box 1700, STN CSC, Victoria, BC, V8W 2Y2, Canada.
Cogn Res Princ Implic. 2018 Oct 3;3(1):38. doi: 10.1186/s41235-018-0131-6.
Many medical professions require practitioners to perform visual categorizations in domains such as radiology, dermatology, and neurology. However, acquiring visual expertise is tedious and time-consuming and the perceptual strategies mediating visual categorization skills are poorly understood. In this paper, the Ease algorithm was developed to predict an item's categorization difficulty (Ease value) based on the item's perceptual similarity to all within-category items versus between-category items in the dataset. In this study, Ease values were used to construct an easy-to-hard and hard-to-easy training schedule for teaching melanoma diagnosis. Whereas previous visual training studies suggest that an easy-to-hard schedule benefits learning outcomes, no studies to date have demonstrated the easy-to-hard advantage with complex, real-world images. In our study, 237 melanoma and benign images were collected for training and testing purposes. The diagnostic accuracy of images was verified by an expert dermatologist. Based on their Ease values, the items were grouped into easy, medium, and hard categories, each containing an equal number of melanoma and benign lesions. During training, participants categorized images of skin lesions as either benign or melanoma and were given corrective feedback after each trial. In the easy-to-hard training condition, participants learned to categorize all the easy items first, followed by the medium items, and finally the hard items. Participants in the hard-to-easy training condition learned items in the reverse order. Post-training results showed that training in both conditions transferred to the classification of new melanoma and benign images. Participants in the easy-to-hard condition showed modest advantages both in the acquisition and retention of the melanoma diagnosis skills, but neither scheduling condition exhibited a gross advantage. The Ease values of the items predicted categorization accuracy after, but not before training, suggesting that the Ease algorithm is a promising tool for optimizing medical training in visual categorization.
许多医学专业要求从业者在放射学、皮肤病学和神经学等领域进行视觉分类。然而，获得视觉专业知识既繁琐又耗时，而且介导视觉分类技能的感知策略还 poorly understood。在本文中，开发了Ease算法，以根据数据集中项目与所有类别内项目和类别间项目的感知相似性来预测项目的分类难度（Ease值）。在本研究中，Ease值被用于构建一个从易到难和从难到易的训练计划，用于教授黑色素瘤诊断。尽管先前的视觉训练研究表明，从易到难的训练计划有利于学习成果，但迄今为止，尚无研究证明在复杂的真实世界图像中存在从易到难的优势。在我们的研究中，收集了237张黑色素瘤和良性图像用于训练和测试目的。图像的诊断准确性由一位皮肤科专家进行了验证。根据它们的Ease值，这些项目被分为容易、中等和困难三类，每类都包含相同数量的黑色素瘤和良性病变。在训练过程中，参与者将皮肤病变图像分类为良性或黑色素瘤，并在每次试验后得到纠正反馈。在从易到难的训练条件下，参与者首先学习对所有容易的项目进行分类，然后是中等难度的项目，最后是困难的项目。从难到易训练条件下的参与者则以相反的顺序学习项目。训练后的结果表明，两种条件下的训练都能迁移到对新的黑色素瘤和良性图像的分类中。从易到难条件下的参与者在黑色素瘤诊断技能的获得和保持方面都表现出适度的优势，但两种训练计划条件都没有表现出明显的优势。项目的Ease值在训练后而非训练前预测了分类准确性，这表明Ease算法是优化视觉分类医学训练的一个有前途的工具。
