Zaveri Manit, Kalra Shivam, Babaie Morteza, Shah Sultaan, Damskinos Savvas, Kashani Hany, Tizhoosh H R
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1416-1419. doi: 10.1109/EMBC44109.2020.9175653.
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.
数字成像技术的最新进展已将计算机视觉和机器学习转变为分析病理图像的新工具。这一趋势可以使诊断病理学中的一些任务自动化，并减轻病理学家的工作量。任何癌症诊断程序的最后一步都是由专业病理学家完成的。这些专家使用具有高光学放大倍数的显微镜来观察通过活检获取并固定在载玻片上的组织的微小特征。在不同放大倍数之间切换，并找到他们识别恶性组织存在与否的放大倍数水平很重要。由于大多数病理学家仍使用光学显微镜，与数字扫描仪相比，在许多情况下，显微镜上安装的摄像头用于从重要视野中捕获快照。此类快照存储库通常不包含放大倍数信息。在本文中，我们提取了TCGA数据集中已知放大倍数的图像的深度特征，以训练用于放大倍数识别的分类器。我们将结果与著名的手工特征提取方法LBP进行了比较。当将多层感知器训练为分类器时，所提出的方法实现了96%的平均准确率。
