All course materials were prepared by Mitko Veta and Maxime Lafarge.
- Machine learning fundamentals
- From linear models to deep neural networks
- Convolutional neural networks
- Training neural networks in your web browser using Tensorflow playground
- Machine learning fundamentals
- From linear models to deep neural networks
- Convolutional neural networks
- Linear and logistic regression in Keras
- Fully connected neural networks for image classification in Keras
- Convolutional neural networks for image classification in Keras
- Experimental methodology for training of (deep) machine learning models
- Modern neural network architectures
- Image segmentation with U-Net
- Mini-competition: segmentation of cardiac MR images (see part 2 of previous exercise)
Some other resources that you might find useful after completing this course or as a preparation.
- Educational module 'Essential Skills for Machine Learning'
- 8P361 Project Imaging bachelor course
- 8DM40 Machine Learning in Medical Imaging and Biology master course
- Oxford Machine Learning course by Nando de Freitas
- Stanford CS231n Convolutional Neural Networks for Visual Recognition course
- Stanford CS229 Machine Learning course
- A survey on deep learning in medical image analysis
- High-performance medicine: the convergence of human and artificial intelligence
A list of datasets published as medical image analysis challenges can be found here.
The Neural Networks video series from the 3Blue1Brown YoutubeChannel.