Specializing Foundation Models via Mixture of Low-Rank Experts for Comprehensive Head CT Analysis
Youngjin Yoo, Han Liu, Bogdan Georgescu, Yanbo Zhang, Sasa Grbic, Michael Baumgartner, Thomas J. Re, Jyotipriya Das, Poikavila Ullaskrishnan, Eva Eibenberger, Andrei Chekkoury, Uttam K. Bodanapally, Savvas Nicolaou, Pina C. Sanelli, Thomas J. Schroeppel, Yvonne W. Lui

TL;DR
This paper introduces MoLRE, a mixture of low-rank experts framework that enhances foundation models for comprehensive head CT analysis by enabling specialized feature adaptation with minimal additional parameters.
Contribution
It proposes a novel MoLRE method that extends LoRA with multiple adapters and soft routing, improving multi-label head CT detection without explicit pathology supervision.
Findings
MoLRE improves performance across diverse models and architectures.
General-purpose and medical-domain models benefit most from MoLRE.
Combining MoLRE with MedGemma achieves an AUC of 0.917.
Abstract
Foundation models pre-trained on large-scale datasets demonstrate strong transfer learning capabilities; however, their adaptation to complex multi-label diagnostic tasks-such as comprehensive head CT finding detection-remains understudied. Standard parameter-efficient fine-tuning methods such as LoRA apply uniform adaptations across pathology types, which may limit performance for diverse medical findings. We propose a Mixture of Low-Rank Experts (MoLRE) framework that extends LoRA with multiple specialized low-rank adapters and unsupervised soft routing. This approach enables conditional feature adaptation with less than 0.5% additional parameters and without explicit pathology supervision. We present a comprehensive benchmark of MoLRE across six state-of-the-art medical imaging foundation models spanning 2D and 3D architectures, general-domain, medical-domain, and head CT-specific…
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Taxonomy
TopicsCOVID-19 diagnosis using AI · Generative Adversarial Networks and Image Synthesis · Radiomics and Machine Learning in Medical Imaging
