Guided Project: Breast Cancer Classification

David Landup
David Landup

In this lesson, we'll be diving into a hands-on project, from start to finish, contemplating what the challenge is, what the reward would be for solving it. Specifically, we'll be classifying benign and malignant Invasive Ductal Carcinoma from histopathology images. If you're unfamiliar with this terminology - no need to worry, it's covered in the guided project.

We'll start out by performing Domain Research, and getting familiar with the domain we're trying to solve a problem in. We'll then proceed with Exploratory Data Analysis, and begin the standard Machine Learning Workflow. For this guide, we'll both be building a CNN from scratch, as well as use pre-defined architectures (such as the EfficientNet family, or ResNet family). Once we benchmark the most promising baseline model - we'll perform hyperparameter tuning, and evaluate the model.

Machine Learning in Medicine

Machine Learning has been increasingly employed in medicine, and is helping save lives from a wide variety of medical conditions. The application of Machine Learning in Medicine is vast, and an extremely complex topic in and of itself, but some of the major areas include:

  • Precision Medicine (Tailoring medicine to individuals)
  • Medical Imaging Diagnosis (Diagnosing conditions based on images, etc.)
  • Drug Discovery (Generating structures such as proteins or drug-like molecules, bioactivity prediction, etc.)
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