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

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.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

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, but is not required. 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

TECHNIQUES

Classification as a machine learning task
Intermediate deep learning concepts such as convolutional neural networks

you will learn

In this liveProject, you will learn important deep learning tools and techniques for data augmentation, semi-supervised learning, and generative modeling. These techniques are highly transferable to a wide range of machine learning roles, especially in the field of computer vision.

Data augmentation built into the image pre-processing pipeline
Pytorch and deep learning on the GPU
Image classification using deep convolutional neural networks
Transfer learning
Generative modeling: variational autoencoders (VAE), generative adversarial
Semi-supervised learning with GANs for image classification

features

Self-paced: You choose the schedule and decide how much time to invest as you build your project.
Project roadmap: Each project is divided into several achievable steps.
Get Help: While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others: For each step, compare your deliverable to the solutions by the author and other participants.
book resources: Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

project outline

Introduction

Prerequisites Test

New module

Get Started

1. Setting up the Development Environment

1.1 Setting up the Development Environment

What is PyTorch?

Immediate vs. Deferred Execution

The PyTorch nn Module

Hardware for Deep Learning

Using Jupyter Notebooks

1.2 Submit Your Work

2. Setting Up an Image Pipeline

2.1 Setting Up an Image Pipeline

Ready, Dataset, Go!

Data Augmentation

2.2 Submit Your Work

3. Training a Supervised Image Classifier

3.1 Training a Supervised Image Classifier

Transfer Learning

A Pre-Trained Network that Recognizes the Subject of an Image

Using Convolutions To Generalize

3.2 Submit Your Work

4. Introduction to Generative Modeling: Variational Autoencoders (VAE)

4.1 Introduction to Generative Modeling: Variational Autoencoders (VAE)

Intro to Generative Modeling with Autoencoders

4.2 Submit Your Work

5. Training an Unsupervised Generative Adversarial Network (GAN)

5.1 Training an Unsupervised Generative Adversarial Network (GAN)

Introduction to GANs

Your First GAN: Generating Handwritten Digits

Deep Convolutional GAN

5.2 Submit Your Work

6. Training the Semi-Supervised GAN for Melanoma Image Classification

6.1 Training the Semi-Supervised GAN for Melanoma Image Classification

Semi-Supervised GAN

6.2 Submit Your Work

Summary

Project Conclusions

FAQs

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