A Saliency-based Clustering Framework for Identifying Aberrant
  Predictions
Aina Tersol Montserrat Alexander R. Loftus Yael Daihes
In machine learning, classification tasks serve as the cornerstone of a wide
range of real-world applications. Reliable, trustworthy classification is
particularly intricate in biomedical settings, where the ground truth is often
inherently uncertain and relies on high degrees of human expertise for
labeling. Traditional metrics such as precision and recall, while valuable, are
insufficient for capturing the nuances of these ambiguous scenarios. Here we
introduce the concept of aberrant predictions, emphasizing that the nature of
classification errors is as critical as their frequency. We propose a novel,
efficient training methodology aimed at both reducing the misclassification
rate and discerning aberrant predictions. Our framework demonstrates a
substantial improvement in model performance, achieving a 20\% increase in
precision. We apply this methodology to the less-explored domain of veterinary
radiology, where the stakes are high but have not been as extensively studied
compared to human medicine. By focusing on the identification and mitigation of
aberrant predictions, we enhance the utility and trustworthiness of machine
learning classifiers in high-stakes, real-world scenarios, including new
applications in the veterinary world.