Practical Coordinator	Nicola  Dinsdale
Lecturer	Ana Namburete
Degrees	Schedule B1 (CS&P) — Computer Science and Philosophy Schedule A2 — Computer Science Schedule B1 — Computer Science Schedule A2(M&CS) — Mathematics and Computer Science Schedule B1(M&CS) — Mathematics and Computer Science
Term	Hilary Term 2024  (16 lectures)

Overview

Neuroscience 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.

Learning outcomes

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.

Prerequisites

The emphasis of this introductory course will be on the application of deep learning to healthcare. Nonetheless, it is important to have good mathematical background in the following topics:

Essential (as covered in first-year Computer Science course)

• Linear Algebra
• Continuous Mathematics 

Desirable

• 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

Synopsis

Introduction 

History of deep learning, and overview of healthcare applications

Connectionism, feedforward networks, activation functions

Backpropagation, optimisation techniques

 

From ANNs to CNNs

Sparse connectivity, weight-sharing, convolutional neural networks (CNNs)

Residual blocks, encoder-decoder architectures

 

Working with medical data

Practical considerations, data-handling

Techniques for low-data regimes (few-/low-shot learning, domain adaptation, augmentation, dropout)

Performance metrics (Dice, ROC, AUC, Hausdorff)

 

Sequence models, attention, and transformers

Recurrent neural networks, backpropagation through time (BPTT)

Long-short-term memory (LSTM) and Gated recurrent units (GRUs)

Attention, self-attention, transformers, and visual transformers

 

Explainability, privacy, and fairness

Saliency analysis, uncertainty, out-of-distribution detection

Federated learning, differential privacy

Continual learning

 

Reading list

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 

Feedback

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.

Taking our courses

This form is not to be used by students studying for a degree in the Department of Computer Science, or for Visiting Students who are registered for Computer Science courses

Other matriculated University of Oxford students who are interested in taking this, or other, courses in the Department of Computer Science, must complete this online form by 17.00 on Friday of 0th week of term in which the course is taught. Late requests, and requests sent by email, will not be considered. All requests must be approved by the relevant Computer Science departmental committee and can only be submitted using this form.