Healthcare applications for Machine Learning are extremely promising, helping healthcare practitioners in diagnosing serious diseases.
What it does
The application has a web interface, where a user can upload an image of a histopathology slide, and get a predication for if contains being benign or malignant tumor cells.
How I built it
Using Pytorch, we trained our model using the PCam slides dataset from https://github.com/basveeling/pcam , and built a flask application and a user interface for uploading the images, the application is deployed to Heroku.
Challenges I ran into
Optimizing the parameters to obtain high accuracy
Accomplishments that I'm proud of
Taking my first steps to experience the already promising machine learning application in healthcare
What I learned
The process of deploying training and optimizing machine learning models.
What's next for Predict Health - Cancer Predication with PyTorch
Optimize for higher accuracy
 B. S. Veeling, J. Linmans, J. Winkens, T. Cohen, M. Welling. "Rotation Equivariant CNNs for Digital Pathology". arXiv:1806.03962
A citation of the original Camelyon16 dataset paper is appreciated as well:
 Ehteshami Bejnordi et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA: The Journal of the American Medical Association, 318(22), 2199–2210. doi:jama.2017.14585