识别显微镜快照中的放大倍率
Recognizing Magnification Levels in Microscopic Snapshots
摘要：
数字成像的最新进展已经将计算机视觉和机器学习转变为用于分析病理学图像的新工具。这种趋势可以使诊断病理学中的一些任务自动化，并提高病理学家的工作量。任何癌症诊断程序的最后一步都由专家病理学家执行。这些专家使用具有高光学放大率的显微镜观察通过活检获得并固定在载玻片上的组织的微小特征。在不同的放大倍数之间切换，并找到识别恶性组织存在或不存在的放大倍数水平是很重要的。由于大多数病理学家仍然使用光学显微镜，与数字扫描仪相比，在许多情况下，显微镜上安装的相机用于从重要的视场捕获快照。此类快照的存储库通常不包含放大信息。在本文中，我们提取的图像的深度特征的TCGA数据集与已知的放大倍率训练分类器的放大倍率识别。我们比较了结果与LBP，一个众所周知的手工特征提取方法。当一个多层感知器被训练成一个分类器时，所提出的方法达到了96%的平均准确率。
英文摘要：
Recent advances in digital imaging has transformed computer vision and machine learning to new tools for analyzing pathology images. This trend could automate some of the tasks in the diagnostic pathology and elevate the pathologist workload. The final step of any cancer diagnosis procedure is performed by the expert pathologist. These experts use microscopes with high level of optical magnification to observe minute characteristics of the tissue acquired through biopsy and fixed on glass slides. Switching between different magnifications, and finding the magnification level at which they identify the presence or absence of malignant tissues is important. As the majority of pathologists still use light microscopy, compared to digital scanners, in many instance a mounted camera on the microscope is used to capture snapshots from significant field-of-views. Repositories of such snapshots usually do not contain the magnification information. In this paper, we extract deep features of the images available on TCGA dataset with known magnification to train a classifier for magnification recognition. We compared the results with LBP, a well-known handcrafted feature extraction method. The proposed approach achieved a mean accuracy of 96% when a multi-layer perceptron was trained as a classifier.
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。
