Regularizing CNNs using Confusion Penalty Based Label Smoothing for Histopathology Images
This incremental method addresses overconfidence issues in CNNs for medical image analysis, specifically benefiting histopathology applications.
The paper tackles overconfidence in CNNs for histopathology image classification by introducing a confusion penalty-based label smoothing technique, achieving satisfactory results on colorectal histology datasets with improvements shown via reliability diagrams and t-SNE plots.
Deep Learning, particularly Convolutional Neural Networks (CNN), has been successful in computer vision tasks and medical image analysis. However, modern CNNs can be overconfident, making them difficult to deploy in real-world scenarios. Researchers propose regularizing techniques, such as Label Smoothing (LS), which introduces soft labels for training data, making the classifier more regularized. LS captures disagreements or lack of confidence in the training phase, making the classifier more regularized. Although LS is quite simple and effective, traditional LS techniques utilize a weighted average between target distribution and a uniform distribution across the classes, which limits the objective of LS as well as the performance. This paper introduces a novel LS technique based on the confusion penalty, which treats model confusion for each class with more importance than others. We have performed extensive experiments with well-known CNN architectures with this technique on publicly available Colorectal Histology datasets and got satisfactory results. Also, we have compared our findings with the State-of-the-art and shown our method's efficacy with Reliability diagrams and t-distributed Stochastic Neighbor Embedding (t-SNE) plots of feature space.