3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease
  Progression from Longitudinal OCTs
Taha Emre Arunava Chakravarty Antoine Rivail Dmitrii Lachinov Oliver Leingang Sophie Riedl Julia Mai Hendrik P. N. Scholl Sobha Sivaprasad Daniel Rueckert Andrew Lotery Ursula Schmidt-Erfurth Hrvoje Bogunović
Self-supervised learning (SSL) has emerged as a powerful technique for
improving the efficiency and effectiveness of deep learning models. Contrastive
methods are a prominent family of SSL that extract similar representations of
two augmented views of an image while pushing away others in the representation
space as negatives. However, the state-of-the-art contrastive methods require
large batch sizes and augmentations designed for natural images that are
impractical for 3D medical images. To address these limitations, we propose a
new longitudinal SSL method, 3DTINC, based on non-contrastive learning. It is
designed to learn perturbation-invariant features for 3D optical coherence
tomography (OCT) volumes, using augmentations specifically designed for OCT. We
introduce a new non-contrastive similarity loss term that learns temporal
information implicitly from intra-patient scans acquired at different times.
Our experiments show that this temporal information is crucial for predicting
progression of retinal diseases, such as age-related macular degeneration
(AMD). After pretraining with 3DTINC, we evaluated the learned representations
and the prognostic models on two large-scale longitudinal datasets of retinal
OCTs where we predict the conversion to wet-AMD within a six months interval.
Our results demonstrate that each component of our contributions is crucial for
learning meaningful representations useful in predicting disease progression
from longitudinal volumetric scans.