Deep Learning in Healthcare: 2022-2023
OverviewNeuroscience research has inspired deep learning, and with the increasing digitisation of the medical domain, deep learning is set to advance healthcare. Deep learning has the potential to transform healthcare in areas ranging from medical imaging to electronic health records. This course aims to introduce students to recent advances in deep learning in healthcare, and the application of deep learning algorithms to medical data, including practical considerations for adapting models to the diversity of healthcare data. We will first introduce the biological basis for neural networks, the foundations of neural networks, and how the numerical operations are based on concepts from linear algebra, continuous mathematics, and probability, and how they are applied to train supervised models. We will then cover the computational components (e.g. convolutional neural networks) that form the backbone of powerful tools in deep learning, and how these can be deployed in the context of a variety of medical data (e.g. images). We will then cover networks that exploit the sequential (or temporal) structures in the data. Throughout the course, we will cover the practicalities of training neural networks, focusing particularly on applications in the healthcare domain, including discussion of optimisation, scalability, privacy, and fairness.
The goal of this course is to provide an intuition for adapting deep learning algorithms to healthcare data and understanding the subtleties in applying these methods to real-world data. We will also discuss open challenges for future research.
In this course, students will:
- Broaden knowledge and fluency about state-of-art deep learning algorithms.
- Develop practical ability to design and train neural networks, and understand how to adapt models to diverse types of healthcare data.
- Understand practical considerations and domain-specific challenges associated with the use of medical data.
- Demonstrate how to systematically explore a basic deep learning problem.
The emphasis of this course will be on the application of deep learning to healthcare. Nonetheless, it is important to have good mathematical background in the following topics:
- Machine Learning
- Linear Algebra
- Continuous Mathematics
- Proficiency in Python programming or significant experience with an alternative programming language are essential for the practical sessions and the mini-project examination. Experience with Pytorch is desirable.
Lecture 1: History of deep learning, and overview of healthcare applications
Lecture 2: Connectionism, feedforward networks, activation functions
Lecture 3: Backpropagation, optimisation techniques
From ANNs to CNNs
Lecture 4: Sparse connectivity, weight-sharing, convolutions, convolutional neural networks (CNNs)
Lecture 5: Residual blocks, encoder-decoder architectures
Working with medical data
Lecture 6: Practical considerations, data-handling, dimensionality, performance metrics
Lecture 7: Regularisation techniques
Sequence models, attention, and transformers
Lecture 8: Recurrent neural networks, backpropagation through time (BPTT)
Lecture 9: Long-short-term memory (LSTM) and Gated recurrent units (GRUs)
Lecture 10: Attention, self-attention, transformers, and visual transformers
Interpretability and privacy
Lecture 11: Class activation maps, saliency analysis
Lecture 12: Federated learning
Lecture 13: Autoencoders, variational autoencoders (VAEs), generative adversarial networks (GANs)
Fairness and trust
Lecture 14-15: Uncertainty and confound removal
The application of deep learning to healthcare data is still expanding, and there are no textbooks that cover this topic in great depth. As such, the material will be self-contained within the lecture slides, with several references to relevant academic articles.
Where relevant, the lecture slides may refer to the following textbooks:
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016
- Raul Rojas. Neural Networks: A systematic introduction. Springer-Verlag, 1996
Students are formally asked for feedback at the end of the course. Students can also submit feedback at any point here. Feedback received here will go to the Head of Academic Administration, and will be dealt with confidentially when being passed on further. All feedback is welcome.