论文标题
深度学习方法来描述和分类真菌显微镜图像
Deep learning approach to describe and classify fungi microscopic images
论文作者
论文摘要
真菌感染的初步诊断可以依靠微观检查。但是，在许多情况下，由于其视觉相似性，微生物学家不允许通过微生物学家识别该物种。因此，通常需要使用其他生化测试。这涉及额外的成本，并将标识过程扩展到10天。由于免疫抑制患者的死亡率很高，因此实施靶向治疗的实施延迟可能是严重的。在本文中，我们采用基于深神网络和Fisher Vector（Advanced of-of-of-of-of-words方法）的机器学习方法来对各种真菌物种的微观图像进行分类。我们的方法有可能使生化鉴定的最后阶段冗余，将识别过程缩短2-3天，并降低诊断成本。
Preliminary diagnosis of fungal infections can rely on microscopic examination. However, in many cases, it does not allow unambiguous identification of the species by microbiologist due to their visual similarity. Therefore, it is usually necessary to use additional biochemical tests. That involves additional costs and extends the identification process up to 10 days. Such a delay in the implementation of targeted therapy may be grave in consequence as the mortality rate for immunosuppressed patients is high. In this paper, we apply a machine learning approach based on deep neural networks and Fisher Vector (advanced bag-of-words method) to classify microscopic images of various fungi species. Our approach has the potential to make the last stage of biochemical identification redundant, shortening the identification process by 2-3 days, and reducing the cost of the diagnosis.
