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

Han Liu; Bogdan Georgescu; Yanbo Zhang; Youngjin Yoo; Michael Baumgartner; Riqiang Gao; Jianing Wang; Gengyan Zhao; Eli Gibson; Dorin Comaniciu; Sasa Grbic

arXiv · 2512.12887

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

Han Liu·Bogdan Georgescu·Yanbo Zhang·Youngjin Yoo·Michael Baumgartner·Riqiang Gao·Jianing Wang·Gengyan Zhao·Eli Gibson·Dorin Comaniciu·Sasa Grbic

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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 scalable 3D classifier adapted from 2D FMs. Our method scales efficiently to new tasks by adding only lightweight plugins (about 1M parameters per task) on top of a single frozen backbone. This versatile framework also supports multi-view inputs, auxiliary pixel-level supervision, and interpretable heatmap generation. We establish a comprehensive benchmark of 12 tasks covering diverse pathologies, anatomies, and modalities, and systematically analyze state-of-the-art 3D classification techniques. Our analysis reveals key insights: (1) effective adaptation is essential to unlock FM potential, (2) general-purpose FMs can match medical-specific FMs if properly adapted, and (3) 2D-based methods surpass 3D architectures for 3D classification. For the first time, we demonstrate the feasibility of achieving state-of-the-art performance across diverse applications using a single scalable framework (including 1st place in the VLM3D challenge), eliminating the need for separate task-specific models.

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BibTeX RIS

Han Liu, Bogdan Georgescu, Yanbo Zhang, Youngjin Yoo, Michael Baumgartner, Riqiang Gao, Jianing Wang, Gengyan Zhao, Eli Gibson, Dorin Comaniciu, Sasa Grbic. 2026-05-26. Revisiting 2D Foundation Models for Scalable 3D Medical Image Classification. https://arxiv.org/abs/2512.12887

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