用卷积神经网络测定猫网织红细胞百分率
Using Convolutional Neural Networks for Determining Reticulocyte Percentage in Cats
摘要：
人工智能（AI）的最新进展，特别是在计算机视觉（CV）和深度学习（DL）方面，为许多领域的新型系统创造了机会。在过去的几年里，深度学习的应用不仅在自动驾驶和机器人等领域取得了令人印象深刻的成果，而且在医学领域，在某些情况下，它们甚至超过了人类水平的表现。然而，尽管潜力巨大，但在许多领域，特别是在兽医领域，采用基于深度学习的方法仍然缓慢，我们还没有找到任何使用现代卷积神经网络（CNN）在医学图像处理中的研究论文。我们相信，使用基于深度学习的医学成像可以实现兽医学中更准确、更快速、更便宜的诊断。然而，为了做到这一点，这些方法必须让该领域的每个人都能使用，而不仅仅是计算机科学家。为了展示这项技术的潜力，我们展示了兽医学中通常手动完成的现实任务的结果：猫网织红细胞百分比。使用开源Keras实现的Single-Shot MultiBox Detector（SSD）模型架构，并仅在800个标记图像上对其进行训练，我们在预测猫血涂片显微镜图像中聚集网织红细胞的正确数量方面实现了98.7%的准确率。这篇论文背后的主要动机是表明，深度学习不仅可以在这样的任务上接近甚至超过人类水平的表现，而且该领域的任何人都可以实现它，即使没有计算机科学的背景。
英文摘要：
Recent advances in artificial intelligence (AI), specifically in computer vision (CV) and deep learning (DL), have created opportunities for novel systems in many fields. In the last few years, deep learning applications have demonstrated impressive results not only in fields such as autonomous driving and robotics, but also in the field of medicine, where they have, in some cases, even exceeded human-level performance. However, despite the huge potential, adoption of deep learning-based methods is still slow in many areas, especially in veterinary medicine, where we haven't been able to find any research papers using modern convolutional neural networks (CNNs) in medical image processing. We believe that using deep learning-based medical imaging can enable more accurate, faster and less expensive diagnoses in veterinary medicine. In order to do so, however, these methods have to be accessible to everyone in this field, not just to computer scientists. To show the potential of this technology, we present results on a real-world task in veterinary medicine that is usually done manually: feline reticulocyte percentage. Using an open source Keras implementation of the Single-Shot MultiBox Detector (SSD) model architecture and training it on only 800 labeled images, we achieve an accuracy of 98.7% at predicting the correct number of aggregate reticulocytes in microscope images of cat blood smears. The main motivation behind this paper is to show not only that deep learning can approach or even exceed human-level performance on a task like this, but also that anyone in the field can implement it, even without a background in computer science.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
