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When is 3D Worth It? A Resource-Performance Frontier for CNNs and Transformers in Lung CT
arXiv:2606.06950v1 Announce Type: new 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.