Revisiting 2D Foundation Models for Scalable 3D Medical Image Classification 事件

Revisiting 2D Foundation Models for Scalable 3D Medical Image Classification arXiv:2512.12887v3 Announce Type: replace Abstract: 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 introduce AnyMC3D, a s