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In machine learning, classification tasks serve as the cornerstone of a widerange of real-world applications. Reliable, trustworthy classification isparticularly intricate in biomedical settings, where the ground truth is ofteninherently uncertain and relies on high degrees of human expertise forlabeling. Traditional metrics such as precision and recall, while valuable, areinsufficient for capturing the nuances of these ambiguous scenarios. Here weintroduce the concept of aberrant predictions, emphasizing that the nature ofclassification errors is as critical as their frequency. We propose a novel,efficient training methodology aimed at both reducing the misclassificationrate and discerning aberrant predictions. Our framework demonstrates asubstantial improvement in model performance, achieving a 20\% increase inprecision. We apply this methodology to the less-explored domain of veterinaryradiology, where the stakes are high but have not been as extensively studiedcompared to human medicine. By focusing on the identification and mitigation ofaberrant predictions, we enhance the utility and trustworthiness of machinelearning classifiers in high-stakes, real-world scenarios, including newapplications in the veterinary world.

A Saliency-based Clustering Framework for Identifying Aberrant Predictions