用深度学习分析未培养水样显微图像，实现秒级水质安全检测。
A Novel Approach for Testing Water Safety Using Deep Learning Inference of Microscopic Images of Unincubated Water Samples
- 基于显微图像与创新数据增强技术，构建细菌识别模型。
- 在10万张真实水样图像上测试，准确率达93%，召回超94%。
- 成本降至0.44美元/次，适合基层快速筛查与移动端使用。
粪便污染的水体可引发疾病甚至死亡。现有微生物水质检测需24-72小时培养，每项成本20-50美元。本文提出一种新方案（DeepScope），满足联合国儿童基金会理想产品需求标准，单次检测成本约0.44美元。通过消除病原体培养环节，检测时间缩短超过98%。DeepScope构建了细菌及水样显微图像数据集，开发了一种创新图像增强技术，可从一张显微图像生成最多21万亿张训练样本。采用迁移学习和正则化技术训练四个卷积神经网络模型，并在包含10万张未见真实水样图像的现场测试数据集上评估，该数据集来自华盛顿州萨马米什地区的14个水源。精度-召回率分析显示，DeepScope模型准确率达93%，精确度为90%，召回率超过94%。该模型已部署于网络服务器，并开发了Android与iOS移动应用，支持互联网或智能手机端即时水质检测。
原文摘要 · Abstract (English)
Fecal-contaminated water causes diseases and even death. Current microbial water safety tests require pathogen incubation, taking 24-72 hours and costing \$20-\$50 per test. This paper presents a solution (DeepScope) exceeding UNICEF's ideal Target Product Profile requirements for presence/absence testing, with an estimated per-test cost of \$0.44. By eliminating the need for pathogen incubation, DeepScope reduces testing time by over 98\%. In DeepScope, a dataset of microscope images of bacteria and water samples was assembled. An innovative augmentation technique, generating up to 21 trillion images from a single microscope image, was developed. Four convolutional neural network models were developed using transfer learning and regularization techniques, then evaluated on a field-test dataset comprising 100,000 microscope images of unseen, real-world water samples collected from fourteen different water sources across Sammamish, WA. Precision-recall analysis showed the DeepScope model achieves 93\% accuracy, with precision of 90\% and recall exceeding 94\%. The DeepScope model was deployed on a web server, and mobile applications for Android and iOS were developed, enabling Internet-based or smartphone-based water safety testing, with results obtained in seconds.
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