L2M NSERC - AI4Path for the Cancer Marker Ki-67

一种名为Ki-67的核蛋白被认为是在预测和预测癌症相关活检中发挥作用的最重要的因素之一。由于细胞的增殖，Ki-67指数高的肿瘤更有可能有增殖的细胞，这些细胞更有可能因细胞增殖而更快地生长。在测定Ki-67时，病理学家使用的方法有很多种，其中使用最广泛的方法是人工计数和目测(目测法)，这两种方法都被广泛用作测定组织样本中的Ki-67的方法。尽管如此，实现这一目标对人类来说是一个非常耗时和费力的过程。我们的目标是开发一种通用的、但完全自动化的方法，使用深度学习技术分割细胞体的显微Ki-67图像，以及开发一个可以从世界任何地方访问并能够用于进一步分析捕获图像的Web门户(AI4Path)。除了减少人力之外，AI4Path还可以为我们的用户提供许多好处，例如能够以更高程度的可扩展性处理大型数据集，提高再现性，并允许我们以更高程度的再现性处理大型数据集。

A nuclear protein called Ki-67 has been identified as one of the most important factors that play a role in the prediction and prognostication of cancer-related biopsies. As a consequence of the proliferation of cells, tumors with high Ki-67 indices are more likely to have proliferating cells, which have a greater tendency to grow more rapidly as a result of cell proliferation. When it comes to determining Ki-67, there are many different methods that pathologists use, and the most widely used methods are manual counting and visual estimation (eyeballing), both of which are widely used as methods to determine Ki-67 within a tissue sample. In spite of this, it is a very time consuming and laborious process on the part of the human being to achieve this goal. Our goal is to develop a generalist yet fully automated method of segmenting microscopy Ki-67 images for cell bodies using deep learning techniques, as well as to develop a web portal (AI4Path) that can be accessed from anywhere in the world and be able to be used for further analysis of the captured images. In addition to reducing human effort, AI4Path can also provide a number of benefits to our users, such as being able to handle large datasets with a greater degree of scalability, improving reproducibility, and allowing us to handle large datasets with a higher degree of reproducibility.