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
This research provides timely insights into optimizing computational resources for medical AI, a critical concern as AI deployment scales in healthcare.
A strategic reader should care because efficient AI models reduce compute costs and accelerate deployment of critical medical imaging applications, directly impacting healthcare R&D and resource allocation.
The understanding of optimal dimensionality for medical AI models shifts from a default assumption of 3D to a more nuanced view, identifying 2.5D CNNs as potentially more favorable for resource-performance balance.
- · Healthcare AI developers
- · Hospitals and clinics
- · Cloud computing providers (through optimization)
- · Patients (faster, more efficient diagnostics)
- · Developers solely focused on computationally intensive 3D models
Increased adoption of optimized 2.5D CNN architectures for medical imaging due to improved efficiency.
Reduced infrastructure demands for deploying advanced medical AI solutions, accelerating their integration into clinical practice.
Potential reallocation of R&D efforts from purely 3D model development to optimization and hybrid approaches across volumetric data challenges.
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Read at arXiv cs.AI