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首页> 外文会议>Optics and Biophotonics in Low-Resource Settings II >A game-based crowdsourcing platform for rapidly training middle and high school students to perform biomedical image analysis
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A game-based crowdsourcing platform for rapidly training middle and high school students to perform biomedical image analysis

机译：一个基于游戏的众包平台，可快速培训中高中生进行生物医学图像分析

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摘要

We developed an easy-to-use and widely accessible crowd-sourcing tool for rapidly training humans to perform biomedical image diagnostic tasks and demonstrated this platform's ability on middle and high school students in South Korea to diagnose malaria infected red-blood-cells (RBCs) using Giemsa-stained thin blood smears imaged under light microscopes. We previously used the same platform (i.e., BioGames) to crowd-source diagnostics of individual RBC images, marking them as malaria positive (infected), negative (uninfected), or questionable (insufficient information for a reliable diagnosis). Using a custom-developed statistical framework, we combined the diagnoses from both expert diagnosticians and the minimally trained human crowd to generate a gold standard library of malaria-infection labels for RBCs. Using this library of labels, we developed a web-based training and educational toolset that provides a quantified score for diagnosticians/users to compare their performance against their peers and view misdiagnosed cells. We have since demonstrated the ability of this platform to quickly train humans without prior training to reach high diagnostic accuracy as compared to expert diagnosticians. Our initial trial group of 55 middle and high school students has collectively played more than 170 hours, each demonstrating significant improvements after only 3 hours of training games, with diagnostic scores that match expert diagnosticians'. Next, through a national-scale educational outreach program in South Korea we recruited ＞1660 students who demonstrated a similar performance level after 5 hours of training. We plan to further demonstrate this tool's effectiveness for other diagnostic tasks involving image labeling and aim to provide an easily-accessible and quickly adaptable framework for online training of new diagnosticians.
机译：我们开发了易于使用且易于访问的众包工具，用于快速培训人类执行生物医学图像诊断任务，并在韩国的中学生和高中生中证明了该平台诊断疟疾感染的红细胞（RBC）的能力。 ）使用吉姆萨染色的薄血涂片在光学显微镜下成像。我们以前使用同一平台（即BioGames）对单个RBC图像进行众包诊断，将其标记为疟疾阳性（感染），阴性（未感染）或可疑（信息不足以进行可靠诊断）。使用定制开发的统计框架，我们结合了专家诊断人员和受过最少培训的人群的诊断，以生成用于RBC的疟疾感染标签的金标准库。使用此标签库，我们开发了基于Web的培训和教育工具集，可为诊断人员/用户提供量化的分数，以将其与同龄人的表现进行比较并查看误诊的细胞。从那以后，我们已经证明了与专家诊断人员相比，该平台无需预先培训即可快速培训人类的能力，可以达到较高的诊断准确性。我们最初的试验组由55名中学生和高中生组成，他们总共玩了170多个小时，每人仅经过3个小时的训练比赛就显示出明显的进步，其诊断得分与专家诊断学家的水平相符。接下来，通过韩国的一项全国性教育推广计划，我们招募了超过1660名学生，他们在经过5个小时的培训后表现出相似的水平。我们计划进一步证明该工具对涉及图像标签的其他诊断任务的有效性，并旨在为新诊断医生的在线培训提供一个易于访问且快速适应的框架。

著录项

  • 来源
  • 会议地点 San Francisco CA(US)
  • 作者

    Steve Feng; Min-jae Woo; Hannah Kim; Eunso Kim; Sojung Ki; Lei Shao; Aydogan Ozcan;

  • 作者单位

    Electrical Engineering Department, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA,Bioengineering Department, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA;

    Electrical Engineering Department, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA,Bioengineering Department, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA;

    Electrical Engineering Department, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA,Bioengineering Department, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA;

    Electrical Engineering Department, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA,Bioengineering Department, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA;

    Electrical Engineering Department, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA,Bioengineering Department, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA;

    Electrical Engineering Department, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA,Bioengineering Department, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA;

    Electrical Engineering Department, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA,Bioengineering Department, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA,California NanoSystems Institute, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA,Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    crowd-sourcing; serious games; big data; biomedical image analysis; malaria; tele-medicine; BioGames; diagnostician education and training;

    机译：众包严肃的游戏；大数据;生物医学图像分析；疟疾;远程医疗生物游戏；诊断学教育和培训;

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