LMT: Longitudinal Mixing Training, a Framework to Predict Disease
  Progression from a Single Image
Rachid Zeghlache Pierre-Henri Conze Mostafa El Habib Daho Yihao Li Hugo Le boite Ramin Tadayoni Pascal Massin Béatrice Cochener Ikram Brahim Gwenolé Quellec Mathieu Lamard
Longitudinal imaging is able to capture both static anatomical structures and
dynamic changes in disease progression toward earlier and better
patient-specific pathology management. However, conventional approaches rarely
take advantage of longitudinal information for detection and prediction
purposes, especially for Diabetic Retinopathy (DR). In the past years, Mix-up
training and pretext tasks with longitudinal context have effectively enhanced
DR classification results and captured disease progression. In the meantime, a
novel type of neural network named Neural Ordinary Differential Equation (NODE)
has been proposed for solving ordinary differential equations, with a neural
network treated as a black box. By definition, NODE is well suited for solving
time-related problems. In this paper, we propose to combine these three aspects
to detect and predict DR progression. Our framework, Longitudinal Mixing
Training (LMT), can be considered both as a regularizer and as a pretext task
that encodes the disease progression in the latent space. Additionally, we
evaluate the trained model weights on a downstream task with a longitudinal
context using standard and longitudinal pretext tasks. We introduce a new way
to train time-aware models using $t_{mix}$, a weighted average time between two
consecutive examinations. We compare our approach to standard mixing training
on DR classification using OPHDIAT a longitudinal retinal Color Fundus
Photographs (CFP) dataset. We were able to predict whether an eye would develop
a severe DR in the following visit using a single image, with an AUC of 0.798
compared to baseline results of 0.641. Our results indicate that our
longitudinal pretext task can learn the progression of DR disease and that
introducing $t_{mix}$ augmentation is beneficial for time-aware models.