English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In the field of medical imaging, 3D deep learning models play a crucial rolein building powerful predictive models of disease progression. However, thesize of these models presents significant challenges, both in terms ofcomputational resources and data requirements. Moreover, achieving high-qualitypretraining of 3D models proves to be even more challenging. To address theseissues, hybrid 2.5D approaches provide an effective solution for utilizing 3Dvolumetric data efficiently using 2D models. Combining 2D and 3D techniquesoffers a promising avenue for optimizing performance while minimizing memoryrequirements. In this paper, we explore 2.5D architectures based on acombination of convolutional neural networks (CNNs), long short-term memory(LSTM), and Transformers. In addition, leveraging the benefits of recentnon-contrastive pretraining approaches in 2D, we enhanced the performance anddata efficiency of 2.5D techniques even further. We demonstrate theeffectiveness of architectures and associated pretraining on a task ofpredicting progression to wet age-related macular degeneration (AMD) within asix-month period on two large longitudinal OCT datasets.

Pretrained Deep 2.5D Models for Efficient Predictive Modeling from Retinal OCT