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.

翻译：在医学影像领域，3D深度学习模型在构建疾病进展的强大预测模型中发挥着关键作用。然而，这些模型的规模在计算资源和数据需求方面带来了显著挑战。此外，实现3D模型的高质量预训练更具挑战性。为解决这些问题，混合2.5D方法提供了一种利用2D模型高效处理3D体积数据的有效方案。结合2D与3D技术为优化性能同时降低内存需求提供了有前景的途径。本文探索了基于卷积神经网络（CNN）、长短期记忆网络（LSTM）和Transformer组合的2.5D架构。此外，利用近期2D非对比预训练方法的优势，我们进一步提升了2.5D技术的性能与数据效率。我们在两个大型纵向OCT数据集上，针对六个月内预测湿性年龄相关性黄斑变性（AMD）进展的任务，验证了架构及关联预训练的有效性。