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A handheld computer-aided diagnosis system and simulated analysis

机译：掌上电脑辅助诊断系统及仿真分析

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This paper describes a Computer Aided Diagnosis (CAD) system based on cellphone and distributed cluster. One of the bottlenecks in building a CAD system for clinical practice is the storage and process of mass pathology samples freely among different devices, and normal pattern matching algorithm on large scale image set is very time consuming. Distributed computation on cluster has demonstrated the ability to relieve this bottleneck. We develop a system enabling the user to compare the mass image to a dataset with feature table by sending datasets to Generic Data Handler Module in Hadoop, where the pattern recognition is undertaken for the detection of skin diseases. A single and combination retrieval algorithm to data pipeline base on Map Reduce framework is used in our system in order to make optimal choice between recognition accuracy and system cost. The profile of lesion area is drawn by doctors manually on the screen, and then uploads this pattern to the server. In our evaluation experiment, an accuracy of 75% diagnosis hit rate is obtained by testing 100 patients with skin illness. Our system has the potential help in building a novel medical image dataset by collecting large amounts of gold standard during medical diagnosis. Once the project is online, the participants are free to join and eventually an abundant sample dataset will soon be gathered enough for learning. These results demonstrate our technology is very promising and expected to be used in clinical practice.
机译：本文介绍了一种基于手机和分布式集群的计算机辅助诊断（CAD）系统。建立用于临床实践的CAD系统的瓶颈之一是在不同设备之间自由存储和处理大量病理学样本，并且在大型图像集上进行常规模式匹配算法非常耗时。集群上的分布式计算证明了缓解此瓶颈的能力。我们开发了一个系统，通过将数据集发送到Hadoop中的通用数据处理程序模块，该系统使用户能够将海量图像与具有特征表的数据集进行比较，在其中进行模式识别以检测皮肤疾病。为了在识别精度和系统成本之间做出最佳选择，我们在系统中使用了基于Map Reduce框架的数据管道单一和组合检索算法。医生在屏幕上手动绘制病变区域的轮廓，然后将该图案上传到服务器。在我们的评估实验中，通过对100名皮肤病患者进行测试，可以将诊断命中率的准确性提高到75％。通过在医学诊断过程中收集大量金标准液，我们的系统在构建新颖的医学图像数据集方面具有潜在的帮助。项目在线后，参与者可以自由加入，最终很快就会收集到足够的样本数据集以供学习。这些结果表明我们的技术非常有前途，有望在临床实践中使用。

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