Health AI Central
Peer-Reviewed Publication
BMC Med Imaging2026;26(1)February 20, 2026Journal Article

Multimodal contrastive learning for non-invasive chondroid bone tumor classification and grading using radiographs.

Peiying Hua1, Jessica M Sin2, Eric R Henderson3, Jason Ha4, Darcy A Kerr4, Saeed Hassanpour5,6,7
1Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH, 03755, USA.
2Department of Radiology, Dartmouth-Hitchcock Medical Center, Lebanon, NH, 03756, USA.
3Department of Orthopaedics, Dartmouth-Hitchcock Medical Center, Lebanon, NH, 03756, USA.
4Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, 03756, USA.
5Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH, 03755, USA. Saeed.Hassanpour@Dartmouth.edu.
6Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, 03755, USA. Saeed.Hassanpour@Dartmouth.edu.
7Department of Computer Science, Dartmouth College, Hanover, NH, 03755, USA. Saeed.Hassanpour@Dartmouth.edu.
PubMed PMC DOI PDF

Abstract

OBJECTIVE: Diagnosing and grading chondroid bone tumors with radiography is difficult due to overlapping features with benign conditions such as avascular necrosis and fibrous dysplasia. Histology remains the gold standard, but it is invasive, costly, and not always accessible. This pilot study introduces a contrastive learning framework to address two challenges: (1) developing an AI system for a…

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