Medical Image Analysis
using Machine Learning
The aim of this project is to Investigate Machine Learning (ML) methods for medical imaging, hoping to automate tasks that are often challenging, time-consuming, and would otherwise require a high level of medical expertise.
The 2021 EWBOx Machine Learning project aims to encourage students from all backgrounds and levels of experience to immerse themselves in the development of Artificial Intelligence (AI) methods, in particular Machine Learning (ML) methods for medical imaging.
These ML methods aim to facilitate the analysis of medical images for researchers and clinicians, by automating tasks that are often challenging, time-consuming, and would otherwise require a high level of expertise. Their high-performance and flexibility has resulted in an explosive growth of the field, yet AI continues to feel unapproachable for many, which we aim to improve with this project. We partner with DPhil students from the Oxford Institute of Biomedical Engineering and the department of Computer Science to provide an accessible introduction to Python, Pytorch, and ML to the project members during Michaelmas Term, followed by a fun competition to solve medical imaging tasks with ML methods throughout Hilary Term. These tasks will cover a range of difficulty from basic to advanced, in order to accommodate the project for various levels of expertise in programming and ML.
This project is open to anyone interested in machine learning and applying it within biomedical imaging. Some experience with Python is useful but not essential, and no experience with Pytorch is expected - the first half of the project is geared towards learning practical ML within a project setting.
In collaboration with: