From Embeddings to Accuracy: Comparing Foundation Models for Radiographic Classification

Abstract

Foundation models provide robust embeddings for diverse tasks, including medical imaging. We evaluate embeddings from seven general and medical-specific foundation models (e.g., DenseNet121, BiomedCLIP, MedImageInsight, Rad-DINO, CXR-Foundation) for training lightweight adapters in multi-class radiography classification. Using a dataset of 8,842 radiographs across seven classes, we trained adapters with algorithms like K-Nearest Neighbors, logistic regression, SVM, random forest, and MLP. The combination of MedImageInsight embeddings with an SVM or MLP adapter achieved the highest mean area under the curve (mAUC) of 93.1%. This performance was statistically superior to other models, including MedSigLIP with an MLP (91.0%), Rad-DINO with an SVM (90.7%), and CXR-Foundation with logistic regression (88.6%). In contrast, models like BiomedCLIP (82.8%) and Med-Flamingo (78.5%) showed lower performance. Crucially, these lightweight adapters are computationally efficient, training in minutes and performing inference in seconds on a CPU, making them practical for clinical use. A fairness analysis of the top-performing MedImageInsight adapter revealed minimal performance disparities across patient gender (within 1.8%) and age groups (std. dev < 1.4%), with no significant statistical differences. These findings confirm that embeddings from specialized foundation models, particularly MedImageInsight, can power accurate, efficient, and equitable diagnostic tools using simple, lightweight adapters.

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