Self-Healing Model from Uncertain Data

One of the biggest issues when training a new model is represented by wrongly annotated datasets which can lead to significant epistemic uncertainty. Such uncertainty can affect the performance and reliability of AI models, especially in medical applications where correct predictions by the model are crucial. We propose an algorithm that measures the uncertainty of each class for each image in a multi-label classification. The algorithm uses the Monte-Carlo (MC) Dropout [1] layer during the model's training and it computes a value named ∊ certainty per image per class. By detecting and addressing the wrongly annotated images, dataset refinement becomes possible. Additionally, the calculated ∊ certainties are used as soft labels in model retraining. The new model is more robust to data uncertainty, thus resulting in a self-healed model from uncertain data.