ArticleFrontiers in radiology2026
Discriminative feature learning for multiclass lung disease classification using contrastive learning.
Article in Frontiers in radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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13 authors.
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Abstract
Objectives: To develop a contrastive learning model for lung disease classification using discriminative CT imaging embeddings. Methods: A total of 1,187 subjects were included: asthma ( Results: The model achieved a macro-AUC of 89.3% (95% CI: 86.5, 91.8; Conclusions: The proposed model effectively differentiated these three lung diseases and provided meaningful embeddings for phenotype characterization, longitudinal assessment, and qCT metric prediction.
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