Pretrained Deep 2.5D Models for Efficient Predictive Modeling from
  Retinal OCT
Taha Emre Marzieh Oghbaie Arunava Chakravarty Antoine Rivail Sophie Riedl Julia Mai Hendrik P. N. Scholl Sobha Sivaprasad Daniel Rueckert Andrew Lotery Ursula Schmidt-Erfurth Hrvoje Bogunović
In the field of medical imaging, 3D deep learning models play a crucial role
in building powerful predictive models of disease progression. However, the
size of these models presents significant challenges, both in terms of
computational resources and data requirements. Moreover, achieving high-quality
pretraining of 3D models proves to be even more challenging. To address these
issues, hybrid 2.5D approaches provide an effective solution for utilizing 3D
volumetric data efficiently using 2D models. Combining 2D and 3D techniques
offers a promising avenue for optimizing performance while minimizing memory
requirements. In this paper, we explore 2.5D architectures based on a
combination of convolutional neural networks (CNNs), long short-term memory
(LSTM), and Transformers. In addition, leveraging the benefits of recent
non-contrastive pretraining approaches in 2D, we enhanced the performance and
data efficiency of 2.5D techniques even further. We demonstrate the
effectiveness of architectures and associated pretraining on a task of
predicting progression to wet age-related macular degeneration (AMD) within a
six-month period on two large longitudinal OCT datasets.