论文标题
不透明液体疫苗的自动检查
The Automated Inspection of Opaque Liquid Vaccines
论文作者
论文摘要
在制药行业中，筛查含有悬架的不透明疫苗目前是训练有素的人类视觉检查员执行的手动任务。我们表明，深度学习可用于有效地自动化此过程。需要进行移动对比度，以区分异常与其他颗粒，反射和含量放在小瓶表面上。我们训练3D-CONVNET，以预测包含异常情况的20帧视频样本的可能性。我们的无主张数据集由使用制药公司Hal Allergy Group提供的小瓶记录的手工标记的样品组成。我们训练了十个随机初始化的3D convnets，以提供基准，分别观察到阳性样品（包含异常）和阴性（无异常）样品的平均AUROC评分为0.94和0.93。使用帧完成生成对抗网络，我们：（i）引入一种用于计算显着性图的算法，我们用来验证3D-CONVNET确实在识别异常； （ii）使用显着性图提出了一种新型的自我训练方法，以确定多个网络是否同意异常位置。我们的自我训练方法使我们能够通过标记217,888个其他样本来增强数据集。经过增强数据集培训的3D-CONVNET仅在我们仅在未加入的数据集上训练时就可以改善我们获得的结果。
In the pharmaceutical industry the screening of opaque vaccines containing suspensions is currently a manual task carried out by trained human visual inspectors. We show that deep learning can be used to effectively automate this process. A moving contrast is required to distinguish anomalies from other particles, reflections and dust resting on a vial's surface. We train 3D-ConvNets to predict the likelihood of 20-frame video samples containing anomalies. Our unaugmented dataset consists of hand-labelled samples, recorded using vials provided by the HAL Allergy Group, a pharmaceutical company. We trained ten randomly initialized 3D-ConvNets to provide a benchmark, observing mean AUROC scores of 0.94 and 0.93 for positive samples (containing anomalies) and negative (anomaly-free) samples, respectively. Using Frame-Completion Generative Adversarial Networks we: (i) introduce an algorithm for computing saliency maps, which we use to verify that the 3D-ConvNets are indeed identifying anomalies; (ii) propose a novel self-training approach using the saliency maps to determine if multiple networks agree on the location of anomalies. Our self-training approach allows us to augment our data set by labelling 217,888 additional samples. 3D-ConvNets trained with our augmented dataset improve on the results we get when we train only on the unaugmented dataset.
