L2M NSERC - AI4Path for the Cancer Marker Ki-67

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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.