When is 3D Worth It? A Resource-Performance Frontier for CNNs and Transformers in Lung CT

arXiv:2606.06950v1 Announce Type: cross Abstract: Three-dimensional models are widely assumed preferable for volumetric medical imaging, yet their practical value depends on whether performance gains justify added computational cost and complexity. Rather than proposing a new architecture, we study how input dimensionality (2D, 2.5D, 3D) affects model behavior across convolutional neural networks (CNNs) and Vision Transformers (ViTs) under a fixed training protocol. Using a leakage-free NLST cohort (n = 1,977) with supporting LIDC-IDRI data, we find that the 2.5D CNN offers the most favorable

Source: arXiv cs.AI — read the full report at the original publisher.

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