Inspiration: To develop a fast and efficient way of detecting bio-markers for diagnosis of breast cancer in women
What it does: Our program has a set of images of cells undergoing mitotic divisions which are good biomarkers for prognosis of breast cancer. We take in the image of the breast tissue, break the image down to the size in which we have the standard atypia cell images, and then compare for any presence of abnormality in the breast cells
How we built it: Collecting images of mitotic cells and using Wolfram Alpha's machine learning algorithm
Challenges we ran into: Searching for criteria for a cell to be cancerous, comparing the atypia cells to breast cells
Accomplishments that we're proud of: learning things in short time, coming up with algorithms for Biomaker detection, targeting a real world complex problem
What we learned: Machine learning, using Wolfram Alpha's Development platform
What's next for Detection of breast cancer based on image analysis: Coming up with an exhaustive set of criteria for cancerous breast cells, taking into account the orientation of the cells and convolutions, making it into a user friendly software
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