Longitudinal Self-supervised Learning Using Neural Ordinary Differential
  Equation
Rachid Zeghlache Pierre-Henri Conze Mostafa El Habib Daho Yihao Li Hugo Le Boité Ramin Tadayoni Pascal Massin Béatrice Cochener Ikram Brahim Gwenolé Quellec Mathieu Lamard
Longitudinal analysis in medical imaging is crucial to investigate the
progressive changes in anatomical structures or disease progression over time.
In recent years, a novel class of algorithms has emerged with the goal of
learning disease progression in a self-supervised manner, using either pairs of
consecutive images or time series of images. By capturing temporal patterns
without external labels or supervision, longitudinal self-supervised learning
(LSSL) has become a promising avenue. To better understand this core method, we
explore in this paper the LSSL algorithm under different scenarios. The
original LSSL is embedded in an auto-encoder (AE) structure. However,
conventional self-supervised strategies are usually implemented in a
Siamese-like manner. Therefore, (as a first novelty) in this study, we explore
the use of Siamese-like LSSL. Another new core framework named neural ordinary
differential equation (NODE). NODE is a neural network architecture that learns
the dynamics of ordinary differential equations (ODE) through the use of neural
networks. Many temporal systems can be described by ODE, including modeling
disease progression. We believe that there is an interesting connection to make
between LSSL and NODE. This paper aims at providing a better understanding of
those core algorithms for learning the disease progression with the mentioned
change. In our different experiments, we employ a longitudinal dataset, named
OPHDIAT, targeting diabetic retinopathy (DR) follow-up. Our results demonstrate
the application of LSSL without including a reconstruction term, as well as the
potential of incorporating NODE in conjunction with LSSL.