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.

翻译：尽管现有多种方法可解决 curated 数据集中的分类问题，但由于数据存在偏倚或模糊性，这些方法在生物医学应用中往往表现不足。当从脊柱数据推断椎体高度缩减（临床公认Genant评分的关键组成部分）时，此类困难尤为显著。尽管半监督学习、候选提议机制与类别混合等策略可能提供部分解决方案，但目前尚缺乏明确且优越的方法。本文提出一套用于应对这些挑战的通用策略流程图，并通过构建包含30余万条标注的脊柱骨折数据集验证其应用效果。该工作推动了分类问题向临床意义评分的转化，并深化了对椎体高度缩减机制的理解。