Semi-Supervised GANs for Melanoma Detection

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prerequisites
intermediate Python • intermediate deep learning • beginner PyTorch • basics of neural networks
skills learned
generative modeling with unsupervised GANs • semi-supervised learning with GANs
Olga Petrova
1 week · 8-10 hours per week · INTERMEDIATE
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The project goes straight to the point and provides focused hints that really help.

Mikael Dautrey, Executive, Isitix
Look inside
In this liveProject, you’ll utilize PyTorch and powerful semi-supervised learning techniques to construct an advanced image classifier that can tell whether a 32x32 pixel photo of a mole is melanoma-positive—despite working with a very small labelled dataset. You’ll set up your image preprocessing pipeline, feed data into your PyTorch model, and then train a semi-supervised GAN model on both labeled and unlabeled datasets.
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. No prior experience in generative modeling, including GANs, is assumed. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Intermediate Python
  • Basics of PIL
  • Basics of Matplotlib
  • Basics of NumPy
  • Beginner PyTorch
TECHNIQUES
  • Training and evaluating (supervised) deep learning models
  • Basics of neural networks
  • Intermediate deep learning concepts such as convolutional neural networks
  • Basics of linear algebra and statistics

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