Fundamental performance limit without modeling enumeration anomalies

Establish whether vertebra labeling methods that do not explicitly model thoracic and lumbar enumeration anomalies (thoracic enumeration anomalies and lumbar enumeration anomalies) possess an intrinsic ceiling on achievable performance when evaluated on datasets that include such anomalies, and clarify the nature of this limit if it exists.

Background

The paper demonstrates that models trained without correctly labeled enumeration anomalies perform notably worse, indicating that ignoring these anomalies degrades labeling accuracy. Building on this observation, the authors posit a hypothesis that there exists a fundamental performance ceiling for methods that do not explicitly account for enumeration anomalies, due to the inherent contradiction between anomalous and non-anomalous labels.

Validating or refuting this hypothesized ceiling would clarify whether further gains are possible without explicit anomaly modeling or whether anomaly-aware strategies are necessary for high-accuracy vertebra labeling.