ANC Workshop - 24/03/2020


Arno Onken


Prof Sotirios Tsaftaris


Disentangled representation learning in healthcare applications


The detection of disease, segmentation of anatomy and other classical image analysis tasks, have seen incredible improvements due to deep learning. Yet these advances need lots of data: for every new task, new imaging scan, new hospital, more training data are needed.  In this talk, I will show how deep neural networks can learn latent and disentangled embeddings suitable for several analysis tasks. Within a multi-task learning setting I will show that the same framework can learn embeddings drawing supervision from self-supervised tasks that use reconstruction and also temporal dynamics, and weakly supervised tasks obtaining supervision from health records [1,2]. I will then present an extension of this framework on multi-modal (multi-view) learning and inference [3]. I will then discuss how different architectural choices affect disentanglement [3] and highlight issues that raise the need for (new) metrics for assessing disentanglement in content/style disentanglement settings. Time permitting, I will present a challenging auto-regressive task: learning to age the human brain [4].  I will conclude by highlighting challenges for deep learning in healthcare in general.

Papers that will be discussed (in approximate order):

  1. A. Chartsias, T. Joyce, G. Papanastasiou, S. Semple, M. Williams, D. Newby, R. Dharmakumar, S.A. Tsaftaris, 'Disentangled Representation Learning in Cardiac Image Analysis,' Medical Image Analysis, Vol 58, Dec 2019
  2. G. Valvano, A. Chartsias, A. Leo, S.A. Tsaftaris, 'Temporal Consistency Objectives Regularize the Learning of Disentangled Representations,' First MICCAI Workshop, DART 2019, in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019.
  3. A. Chartsias, G. Papanastasiou, C. Wang, S. Semple, D. Newby, R. Dharmakumar, S.A. Tsaftaris, Disentangle, align and fuse for multimodal and zero-shot image segmentation,'
  4. T. Xia, A. Chartsias, S.A. Tsaftaris, 'Consistent Brain Ageing Synthesis,' MICCAI 2019.


Prof. Sotirios A. Tsaftaris is currently Canon Medical/Royal Academy of Engineering Research Chair in Healthcare AI, and Chair in Machine Learning and Computer Vision at the University of Edinburgh (UK). He is also a Turing Fellow with the Alan Turing Institute. Previously he held faculty positions with IMT Institute for Advanced Studies Lucca (Italy) and Northwestern University (USA).   He has published extensively, particularly in interdisciplinary fields, with more than 140 journal and conference papers in his active record. His research interests are machine learning, computer vision, image analysis and processing.  Additional information:<> (Personal site)



Mar 24 2020 -

ANC Workshop - 24/03/2020

Disentangled representation learning in healthcare applications

G.03, IF