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本文引用的文献

1
When does repeated search in scenes involve memory? Looking at versus looking for objects in scenes.当在场景中反复搜索时涉及记忆吗?在场景中寻找与查看物体。
J Exp Psychol Hum Percept Perform. 2012 Feb;38(1):23-41. doi: 10.1037/a0024147. Epub 2011 Jun 20.
2
Scrambled eyes? Disrupting scene structure impedes focal processing and increases bottom-up guidance.混乱的视觉?破坏场景结构会阻碍焦点处理并增加自下而上的引导。
Atten Percept Psychophys. 2011 Oct;73(7):2008-25. doi: 10.3758/s13414-011-0158-y.
3
When categories collide: accumulation of information about multiple categories in rapid scene perception.当类别碰撞时:快速场景感知中多个类别的信息积累。
Psychol Sci. 2011 Jun;22(6):739-46. doi: 10.1177/0956797611407930. Epub 2011 May 9.
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Optimizing analysis, visualization, and navigation of large image data sets: one 5000-section CT scan can ruin your whole day.优化大型图像数据集的分析、可视化和导航:一次 5000 层的 CT 扫描可以毁了你一整天。
Radiology. 2011 May;259(2):346-62. doi: 10.1148/radiol.11091276.
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Does visual expertise improve visual recognition memory?视觉专业知识能提高视觉识别记忆吗?
Atten Percept Psychophys. 2011 Jan;73(1):30-5. doi: 10.3758/s13414-010-0022-5.
6
Visual search in scenes involves selective and nonselective pathways.场景中的视觉搜索涉及选择性和非选择性途径。
Trends Cogn Sci. 2011 Feb;15(2):77-84. doi: 10.1016/j.tics.2010.12.001. Epub 2011 Jan 10.
7
Perceptual learning in Vision Research.视觉研究中的知觉学习。
Vision Res. 2011 Jul 1;51(13):1552-66. doi: 10.1016/j.visres.2010.10.019. Epub 2010 Oct 23.
8
The relative contribution of scene context and target features to visual search in scenes.场景上下文和目标特征对场景视觉搜索的相对贡献。
Atten Percept Psychophys. 2010 Jul;72(5):1283-97. doi: 10.3758/APP.72.5.1283.
9
The time course of initial scene processing for eye movement guidance in natural scene search.自然场景搜索中眼动引导的初始场景处理的时间进程。
J Vis. 2010 Mar 29;10(3):14.1-13. doi: 10.1167/10.3.14.
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Spotting animals in natural scenes: efficiency of humans and monkeys at very low contrasts.在自然场景中识别动物:人类和猴子在极低对比度下的效率。
Anim Cogn. 2010 May;13(3):405-18. doi: 10.1007/s10071-009-0290-4. Epub 2009 Nov 18.

放射学中的信息学:一眼能看到什么,以及这如何指导医学图像中的视觉搜索?

Informatics in radiology: what can you see in a single glance and how might this guide visual search in medical images?

机构信息

Visual Attention Laboratory, Department of Surgery, Brigham and Women's Hospital, 64 Sidney St, Suite 170, Cambridge, MA 02139-4170, USA.

出版信息

Radiographics. 2013 Jan-Feb;33(1):263-74. doi: 10.1148/rg.331125023. Epub 2012 Oct 25.

DOI:10.1148/rg.331125023
PMID:23104971
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3545617/
Abstract

Diagnostic accuracy for radiologists is above that expected by chance when they are exposed to a chest radiograph for only one-fifth of a second, a period too brief for more than a single voluntary eye movement. How do radiologists glean information from a first glance at an image? It is thought that this expert impression of the gestalt of an image is related to the everyday, immediate visual understanding of the gist of a scene. Several high-speed mechanisms guide our search of complex images. Guidance by basic features (such as color) requires no learning, whereas guidance by complex scene properties is learned. It is probable that both hardwired guidance by basic features and learned guidance by scene structure become part of radiologists' expertise. Search in scenes may be best explained by a two-pathway model: Object recognition is performed via a selective pathway in which candidate targets must be individually selected for recognition. A second, nonselective pathway extracts information from global or statistical information without selecting specific objects. An appreciation of the role of nonselective processing may be particularly useful for understanding what separates novice from expert radiologists and could help establish new methods of physician training based on medical image perception.

摘要

当放射科医生仅观察胸部 X 光片五分之一秒时,其诊断准确性高于偶然情况,因为这段时间太短,不足以进行超过一次的自愿眼球运动。放射科医生如何从第一眼看到图像中获取信息?人们认为,这种对图像整体的专家印象与日常的、即时的对场景要点的视觉理解有关。几种高速机制指导我们对复杂图像进行搜索。基本特征(如颜色)的引导不需要学习,而复杂场景属性的引导则需要学习。很可能,由基本特征和场景结构的学习引导都成为放射科医生专业知识的一部分。通过双通道模型可以最好地解释场景中的搜索:对象识别是通过选择性途径进行的,其中候选目标必须逐个选择进行识别。第二个非选择性途径从全局或统计信息中提取信息,而无需选择特定对象。了解非选择性处理的作用可能对理解区分新手和专家放射科医生特别有用,并有助于基于医学图像感知建立新的医生培训方法。