# Regularizing CNNs using Confusion Penalty Based Label Smoothing for Histopathology Images

arXiv: https://arxiv.org/abs/2403.10881 · HTML: https://mlsanity.com/paper/2403.10881/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 23 references that are in the map.
- First posted on arXiv 16 Mar 2024.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
- [Training Deep Neural Networks on Noisy Labels with Bootstrapping](https://mlsanity.com/paper/1412.6596/) (2014)
- [Mitigating Neural Network Overconfidence with Logit Normalization](https://mlsanity.com/paper/2205.09310/) (2022)
- [Deep Learning for Medical Image Processing: Overview, Challenges and Future](https://mlsanity.com/paper/1704.06825/) (2017)
- [Delving Deep into Label Smoothing](https://mlsanity.com/paper/2011.12562/) (2020)
- [DenseNet: Implementing Efficient ConvNet Descriptor Pyramids](https://mlsanity.com/paper/1404.1869/) (2014)
- [Interpreting Chest X-rays via CNNs that Exploit Hierarchical Disease Dependencies and Uncertainty Labels](https://mlsanity.com/paper/2005.12734/) (2020)
- [Adaptive Label Smoothing](https://mlsanity.com/paper/2009.06432/) (2020)
- [Calibrating Histopathology Image Classifiers using Label Smoothing](https://mlsanity.com/paper/2201.11866/) (2022)

## Built on by (most cited first, 1 of 1)

- [Improving Predictive Confidence in Medical Imaging via Online Label Smoothing](https://mlsanity.com/paper/2510.20011/) (2025)

## Abstract (arXiv)

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.