This research addresses the pervasive challenge of label noise in machine learning, particularly in sensitive domains like medical applications and psychiatry. Label noise, stemming from sources such as inadequate information and human error, poses a significant threat to the accuracy of classification models, especially in medical imaging where mislabeled data can lead to harmful outcomes. In the realm of psychiatry, existing categorizations of psychosis face complexities due to unreliability and heterogeneity. The research aims to achieve two key objectives: first, to develop robust frameworks and algorithms for detecting and rectifying incorrect labels in datasets, focusing on semi-supervised auto-labeling approaches to enhance data homogeneity. Second, the research delves into biomarker discovery for mood and psychosis disorders, seeking to unveil latent patterns within neuroimaging data that could serve as vital biomarkers, revolutionizing diagnostic and classification methods for these complex mental health conditions.