论文标题
使用转移学习来检测和分类微观有孔虫
Towards detection and classification of microscopic foraminifera using transfer learning
论文作者
论文摘要
有孔虫是单细胞海洋生物，可能具有浮游生物或底栖生物。在他们的生命周期中，它们构造了由一个或多个腔室组成的贝壳，这些壳仍然是海洋沉积物中的化石。分类和计数这些化石已成为例如海洋学和气候学。当前，识别和计数微化石的过程是使用显微镜手动执行的，并且非常耗时。因此，开发自动化此过程的方法被认为是在一系列研究领域中重要的。提出了开发可以检测和分类微观有孔虫的深度学习模型的第一步。所提出的模型基于在Imagenet数据集上仔细研究的VGG16模型，并使用传输学习适应了有孔虫任务。此外，引入了一个新型图像数据集，该数据集由微观有孔虫和来自Barents Sea区域的沉积物组成。
Foraminifera are single-celled marine organisms, which may have a planktic or benthic lifestyle. During their life cycle they construct shells consisting of one or more chambers, and these shells remain as fossils in marine sediments. Classifying and counting these fossils have become an important tool in e.g. oceanography and climatology. Currently the process of identifying and counting microfossils is performed manually using a microscope and is very time consuming. Developing methods to automate this process is therefore considered important across a range of research fields. The first steps towards developing a deep learning model that can detect and classify microscopic foraminifera are proposed. The proposed model is based on a VGG16 model that has been pretrained on the ImageNet dataset, and adapted to the foraminifera task using transfer learning. Additionally, a novel image dataset consisting of microscopic foraminifera and sediments from the Barents Sea region is introduced.
