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While numerous methods exist to solve classification problems within curateddatasets, these solutions often fall short in biomedical applications due tothe biased or ambiguous nature of the data. These difficulties are particularlyevident when inferring height reduction from vertebral data, a key component ofthe clinically-recognized Genant score. Although strategies such assemi-supervised learning, proposal usage, and class blending may provide someresolution, a clear and superior solution remains elusive. This paperintroduces a flowchart of general strategy to address these issues. Wedemonstrate the application of this strategy by constructing a vertebralfracture dataset with over 300,000 annotations. This work facilitates thetransition of the classification problem into clinically meaningful scores andenriches our understanding of vertebral height reduction.

Annotating Ambiguous Images: General Annotation Strategy for Image  Classification with Real-World Biomedical Validation on Vertebral Fracture  Diagnosis