Canon Medical/Royal Academy of Engineering Research Chair in Healthcare AI

In the UK alone, currently 7 million people live with cardiovascular disease and this number will increase as the population ages. Under-resourced and under-staffed healthcare systems are struggling with the rising caseload and the large volumes of information being generated. Currently, excitement in Artificial Intelligence (AI) for healthcare is high, because of its potential to help stem this information overload and reduce healthcare costs.

The AI paradigm fuelling this excitement heavily depends on well-curated training data and is largely seen as a black box. In contrast we will:

  1. learn from complex, multimodal, healthcare records with minimal supervision; and
  2. focus on problems underpinning learning data representations optimised to provide a transparent base for the desired diagnoses and predictions. We will then translate these techniques to automated estimation of cardiac biomarkers, disease diagnosis, and most ambitiously, cardiac episode prediction, thus opening roads to preventive care.

News

  • Keynote: Doing More with Less by Disentangled Data Representations, Keynote DART, MICCAI, October 2020.

  • Tutorial: DREAM: Disentangled Representations for Efficient Algorithms for Medical data, MICCAI Tutorial, MICCAI, October 2020.

  • Event: Sotos invited to present at The Global AI Summit, organized by the Saudi Data and Artificial Intelligence Authority, March 2020. [postponed due to COVID-19]

  • News: How can AI help? Reading the language of medicine, Canon Medical Blog, October 2019.

  • Event: Public Event in Japan, British Embassy. AI and Healthcare, October 2019.

  • Keynote: Disentangled Representation Learning in Medical Imaging, Keynote STACOM, MICCAI, October 2019.

  • Tutorial: Simulation and Synthesis, Tutorial at International Conference on Acoustics Speech and Signal Processing (ICASSP), May 2019.

  • News: Interview in Greek Huffington Post, September 2019.

  • News: How can AI help? Reading the language of medicine, Canon Medical Blog, (link)

  • Event: Intervention at Future-proofing society – how digital health and social care can empower and transform lives, Scottish Parliament and IET Event.

  • News: Prof. Tsaftaris featured in School of Engineering News.

  • News: Prof. Tsaftaris featured in RAEng News.

  • Kick-off: We started a new project.

Publications

1. Weakly Supervised Segmentation with Multi-scale Adversarial Attention Gates
G. Valvano, A. Leo, S.A. Tsaftaris
TBA, submitted
Preprint

2. Pseudo-healthy synthesis with pathology disentanglement and adversarial learning
T. Xia, A. Chartsias, S.A. Tsaftaris
Medical Image Analysis, vol. 64, 2020
Full Text Preprint Code

3. Have you forgotten? A method to assess if machine learning models have forgotten data
X. Liu, S.A. Tsaftaris
MICCAI 2020
Preprint

4. Metrics for Exposing the Biases of Content-Style Disentanglement
X. Liu, S. Thermos, G. Valvano, A. Chartsias, A.Q. O'Neil, S.A. Tsaftaris
submitted
Preprint Code

5. INSIDE: Steering Spatial Attention with Non-Imaging Information in CNNs
G. Jacenków, A.Q. O'Neil, B. Mohr, S.A. Tsaftaris
MICCAI 2020
Full Text Preprint Code

6. Disentangled Representations for Domain-generalized Cardiac Segmentation
X. Liu, S. Thermos, A. Chartsias, A.Q. O'Neil, S.A. Tsaftaris
MICCAI 2020, STACOM: Statistical Atlases and Computational Modelling of the Heart
Preprint Code

7. Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling
H. Jiang, C. Wang, A. Chartsias, S.A. Tsaftaris
MICCAI 2020, MyoPS: Multi-sequence CMR based Mycardial Pathology Segmentation Challenge
Preprint Code

8. Semi-supervised Pathology Segmentation with Disentangled Representations
H. Jiang, A. Chartsias, X. Zhang, G. Panastasiou, S. Semple, M. Dweck, D. Semple, R. Dharmakumar, S.A. Tsaftaris
MICCAI 2020, DART: Domain Adaptation and Representation Transfer
Preprint Code

9. Consistent Brain Ageing Synthesis
T. Xia, A. Chartsias, S.A. Tsaftaris
MICCAI 2019
PDF Code

10. Multimodal cardiac segmentation using disentangled representations
A. Chartsias, G. Papanastasiou, C. Wang, C. Stirrat, S. Semple, D. Newby, R. Dharmakumar, S.A. Tsaftaris
MICCAI 2019, STACOM: Statistical Atlases and Computational Models of the Heart
PDF Code

11. Temporal Consistency Objectives Regularize the Learning of Disentangled Representations
G. Valvano, A. Chartsias, A. Leo, S.A. Tsaftaris
MICCAI 2019, DART: Domain Adaptation and Representation Transfer
Preprint Code

12. Conditioning Convolutional Segmentation Architectures with Non-Imaging Data
G. Jacenków, A. Chartsias, B. Mohr, S.A. Tsaftaris
MIDL 2019
Full Text

People

Funding

Generously supported by Canon Medical Research Europe, the Royal Academy of Engineering and the School of Engineering.