Research Associate or Research Fellow in Active Learning for Medical Imaging

Location: St Thomas' Hospital

Contact details: Jorge Cardoso. m.jorge.cardoso@kcl.ac.uk

Job description

Medicine is undergoing a data revolution, with AI being the engine of change. To achieve this, algorithms commonly require a significant amount of labelled data, a process which is very time consuming and expert-user intensive. To facilitate and speed-up this labelling process, human raters can make use of AI algorithms to assist the annotation process. This can be done by suggesting an initial segmentation to be curated or improved by a human rater, by selecting particular slices or subjects that are hard to segment, all with the aim of maximising the AI algorithm labelling accuracy while minimising user interaction time.  

The environment:

The London Medical Imaging & AI Centre for Value-Based Healthcare is a consortium of academic, NHS and industry partners led by King’s and based at St Thomas’ Hospital. Our diverse research teams are training sophisticated artificial intelligence algorithms from a vast wealth of NHS medical images and patient pathway data to create new healthcare tools. For patients, these will provide faster diagnosis, personalised therapies and effective screening across a range of conditions and procedures. 

Through a focus on our experience in value-based healthcare we are examining how AI can be used to optimise triage and target resources to deliver significant financial savings for the NHS and healthcare systems overall.  The centre has been established as part of the UK Government’s Industrial Strategy Challenge Fund, delivered through UK Research and Innovation.

The purpose of this role:

This is an exciting opportunity for an enthusiastic deep learning researcher to push the boundaries of human-AI interaction, active learning, image segmentation, object detection, and image classification.

This role will be part of the AI4VBH Centre and will help deliver on a data labelling infrastructure, comprised of visualisation/contouring software and AI models. More specifically, this post will develop new algorithms and associated software stack to enable AI-assisted annotation for the problems of image segmentation, object detection and image classification. The post holder will focus on technical algorithmic developments such has using model uncertainty for active-learning based image/slice prioritisation, AI-based contouring (similarly to grab-cut), and general-purpose model pretraining to bootstrap segmentation, object detection and classification tasks on many different body parts and image modalities.

These algorithms shall be integrated and deployed into the AI4VBHC centre infrastructure as a proof-of-concept, utilising the data management and computational infrastructure of the AI centre.

This post will be offered on a fixed-term contract for 2 years 

This is a full-time post

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