Semi-Supervised Deep Learning with GANs for Melanoma Detection

prerequisites
intermediate Python • intermediate NumPy • beginner PyTorch • basics of deep learning (CNNs)
skills learned
generative modeling • transfer learning • image classification with deep CNNs • semi-supervised learning with GANs
Olga Petrova
6 weeks · 6-10 hours per week · INTERMEDIATE
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In this liveProject, you’ll take on the role of a computer vision engineer creating a proof-of-concept for a mobile app with world-changing potential. The goal of the app is to capture a picture of a skin mole, and then calculate the likelihood of it being a cancerous melanoma in need of medical intervention. Your mission is to build a model that can perform this image classification using low-resolution photos from a phone’s camera. You are provided with a training dataset, however, only a small fraction of the images have been labeled. To overcome this challenge, you will employ data augmentation techniques, transfer learning and semi-supervised learning with GANs to produce your final model.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Olga Petrova
Olga Petrova is a machine learning engineer at Scaleway, a French cloud provider, where her focus lies on deep learning R&D. Previously, she has worked as a researcher in theoretical physics, looking into the applications of artificial intelligence to quantum systems. Olga has a Ph.D. from Johns Hopkins University, and a B.S. from Worcester Polytechnic Institute. She enjoys blogging about the latest advancements in AI.

prerequisites

This liveProject is for intermediate Python programmers with some deep learning experience. Knowledge of PyTorch will be helpful. No prior experience in generative modeling, including GANs, is assumed. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Basics of PIL
  • Basics of Matplotlib
  • Intermediate Python
  • Intermediate NumPy
  • Beginner PyTorch
TECHNIQUES
  • Classification as a machine learning task
  • Intermediate deep learning concepts such as convolutional neural networks

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