使用专家协议作为黄金标准自动评估图像中的生物污垢
Automating the assessment of biofouling in images using expert agreement as a gold standard
摘要：
生物污垢是指浸入水中的表面上的生物体的积累。国际航运业特别关注这一问题，因为它增加了燃料成本，并为非本地海洋物种在新区域定居提供了一条途径，从而带来了生物安全风险。在司法管辖区内，加强生物污损风险管理法规的兴趣越来越大，但进行水中检查和评估收集的数据以确定船体的生物污损状态是昂贵的。机器学习非常适合解决后一个挑战，在这里，我们应用深度学习来自动分类水中检查的图像，以识别污垢的存在和严重程度。我们结合了几个数据集，获得了从水中调查中收集的10，000多张图像，这些图像由一组生物污损专家进行了注释。我们比较了三位专家对这些图像的120个样本子集的注释，发现它们显示出89%的一致性（95%CI：87-92%）。随后由其中一位专家对整个数据集进行标记，与该组专家的一致性水平相似，我们将其定义为表现最多差5%（p=0.009-0.054）。使用这些专家标签，我们能够训练一个深度学习模型，该模型也与专家组相似（p=0.001-0.014），证明使用这种方法自动分析图像中的生物污垢是可行和有效的。
英文摘要：
Biofouling is the accumulation of organisms on surfaces immersed in water. It is of particular concern to the international shipping industry because it increases fuel costs and presents a biosecurity risk by providing a pathway for non-indigenous marine species to establish in new areas. There is growing interest within jurisdictions to strengthen biofouling risk-management regulations, but it is expensive to conduct in-water inspections and assess the collected data to determine the biofouling state of vessel hulls. Machine learning is well suited to tackle the latter challenge, and here we apply deep learning to automate the classification of images from in-water inspections to identify the presence and severity of fouling. We combined several datasets to obtain over 10,000 images collected from in-water surveys which were annotated by a group biofouling experts. We compared the annotations from three experts on a 120-sample subset of these images, and found that they showed 89% agreement (95% CI: 87-92%). Subsequent labelling of the whole dataset by one of these experts achieved similar levels of agreement with this group of experts, which we defined as performing at most 5% worse (p=0.009-0.054). Using these expert labels, we were able to train a deep learning model that also agreed similarly with the group of experts (p=0.001-0.014), demonstrating that automated analysis of biofouling in images is feasible and effective using this method.
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