Annotating Ambiguous Images: General Annotation Strategy for Image
  Classification with Real-World Biomedical Validation on Vertebral Fracture
  Diagnosis
Lars Schmarje Vasco Grossmann Claudius Zelenka Reinhard Koch
While numerous methods exist to solve classification problems within curated
datasets, these solutions often fall short in biomedical applications due to
the biased or ambiguous nature of the data. These difficulties are particularly
evident when inferring height reduction from vertebral data, a key component of
the clinically-recognized Genant score. Although strategies such as
semi-supervised learning, proposal usage, and class blending may provide some
resolution, a clear and superior solution remains elusive. This paper
introduces a flowchart of general strategy to address these issues. We
demonstrate the application of this strategy by constructing a vertebral
fracture dataset with over 300,000 annotations. This work facilitates the
transition of the classification problem into clinically meaningful scores and
enriches our understanding of vertebral height reduction.