UniCAD: Efficient and Extendable Architecture for Multi-Task Computer-Aided Diagnosis System
Journal:
arXiv
Published Date:
May 14, 2025
Abstract
The growing complexity and scale of visual model pre-training have made
developing and deploying multi-task computer-aided diagnosis (CAD) systems
increasingly challenging and resource-intensive. Furthermore, the medical
imaging community lacks an open-source CAD platform to enable the rapid
creation of efficient and extendable diagnostic models. To address these
issues, we propose UniCAD, a unified architecture that leverages the robust
capabilities of pre-trained vision foundation models to seamlessly handle both
2D and 3D medical images while requiring only minimal task-specific parameters.
UniCAD introduces two key innovations: (1) Efficiency: A low-rank adaptation
strategy is employed to adapt a pre-trained visual model to the medical image
domain, achieving performance on par with fully fine-tuned counterparts while
introducing only 0.17% trainable parameters. (2) Plug-and-Play: A modular
architecture that combines a frozen foundation model with multiple
plug-and-play experts, enabling diverse tasks and seamless functionality
expansion. Building on this unified CAD architecture, we establish an
open-source platform where researchers can share and access lightweight CAD
experts, fostering a more equitable and efficient research ecosystem.
Comprehensive experiments across 12 diverse medical datasets demonstrate that
UniCAD consistently outperforms existing methods in both accuracy and
deployment efficiency. The source code and project page are available at
https://mii-laboratory.github.io/UniCAD/.
