AI RESEARCH
Revisiting 2D Foundation Models for Scalable 3D Medical Image Classification
arXiv CS.CV
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ArXi:2512.12887v2 Announce Type: replace 3D medical image classification is essential for modern clinical workflows. Medical foundation models (FMs) have emerged as a promising approach for scaling to new tasks, yet current research suffers from three critical pitfalls: data-regime bias, suboptimal adaptation, and insufficient task coverage. In this paper, we address these pitfalls and