通过低成本光学和 基于卷积神经网络的验证

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摘要翻译：
一种低成本、可靠和简单的血红蛋白测量机制将在现代卫生基础设施中发挥关键作用。对于基于光度计的便携式血红蛋白检测仪来说，样品的一致性获取一直是一个长期的技术障碍，该检测仪依赖于微型比色管和干化学。试管感应区中的任何微粒（如完整的红细胞、微泡等）都会严重影响光学吸收剖面，而商用血红蛋白计缺乏自动检测缺陷样本的能力。我们介绍了一种便携式、低成本和开放平台的地面开发，其精度与医疗级设备相当，并添加了基于CNN的图像处理用于快速样本生存能力预检查。结果表明，该平台的精密度最接近0.18[g/dl]$,与文献报道的血红蛋白吸收曲线的相关系数为0.945,对制备条件差的样品的检测准确度为97%。我们看到开发的血红蛋白设备/毫升平台在农村医学中具有巨大的意义，并认为它是稳健的深度学习光学光谱学的一个极好的跳板：一个目前尚未开发的用于检测无数分析物的数据来源。
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英文标题：
《Rapid point-of-care Hemoglobin measurement through low-cost optics and
  Convolutional Neural Network based validation》
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作者：
Chris Wu, Tanay Tandon
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最新提交年份：
2017
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分类信息：

一级分类：Physics        物理学
二级分类：Medical Physics        医学物理学
分类描述：Radiation therapy. Radiation dosimetry. Biomedical imaging modelling.  Reconstruction, processing, and analysis. Biomedical system modelling and analysis. Health physics. New imaging or therapy modalities.
放射治疗。辐射剂量学。生物医学成像建模。重建、处理和分析。生物医学系统建模与分析。健康物理学。新的成像或治疗方式。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
  A low-cost, robust, and simple mechanism to measure hemoglobin would play a critical role in the modern health infrastructure. Consistent sample acquisition has been a long-standing technical hurdle for photometer-based portable hemoglobin detectors which rely on micro cuvettes and dry chemistry. Any particulates (e.g. intact red blood cells (RBCs), microbubbles, etc.) in a cuvette's sensing area drastically impact optical absorption profile, and commercial hemoglobinometers lack the ability to automatically detect faulty samples. We present the ground-up development of a portable, low-cost and open platform with equivalent accuracy to medical-grade devices, with the addition of CNN-based image processing for rapid sample viability prechecks. The developed platform has demonstrated precision to the nearest $0.18[g/dL]$ of hemoglobin, an R^2 = 0.945 correlation to hemoglobin absorption curves reported in literature, and a 97% detection accuracy of poorly-prepared samples. We see the developed hemoglobin device/ML platform having massive implications in rural medicine, and consider it an excellent springboard for robust deep learning optical spectroscopy: a currently untapped source of data for detection of countless analytes.
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PDF链接：
https://arxiv.org/pdf/1712.00174

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关键词：神经网络 神经网 Construction Architecture Presentation robust optical 检测 low accuracy