The methods proposed in the context of Alzheimer's disease detection, namely Soft Target Distillation (SoTD) and Instance-level Re-balancing (InRe), can be effectively adapted to address within-class variation in various healthcare applications. For instance, in the realm of cancer detection, patients may present with varying degrees of tumor aggressiveness or different responses to treatment, leading to significant within-class variation. By employing SoTD, models can be trained using soft labels that reflect the severity of the disease, allowing for a more nuanced classification that captures the continuum of cancer stages.
Similarly, InRe can be utilized in chronic disease management, such as diabetes or cardiovascular diseases, where patient responses to treatment can vary widely. By rebalancing the training data to account for the distribution of patient characteristics and treatment responses, models can be better equipped to handle the inherent variability in patient populations. Furthermore, these methods can be extended to mental health applications, where symptoms may manifest differently across individuals within the same diagnostic category. By leveraging the principles of SoTD and InRe, healthcare applications can achieve more accurate and personalized predictions, ultimately improving patient outcomes.