# Improved Trainable Calibration Method for Neural Networks on Medical Imaging Classification

arXiv: https://arxiv.org/abs/2009.04057 · HTML: https://mlsanity.com/paper/2009.04057/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2020.

## Built on by (most cited first, 30 of 37)

- [Calibration in Deep Learning: A Survey of the State-of-the-Art](https://mlsanity.com/paper/2308.01222/) (2023)
- [A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration](https://mlsanity.com/paper/2203.13834/) (2022)
- [A Consistent and Differentiable Lp Canonical Calibration Error Estimator](https://mlsanity.com/paper/2210.07810/) (2022)
- [Towards Improving Calibration in Object Detection Under Domain Shift](https://mlsanity.com/paper/2209.07601/) (2022)
- [Your Pre-trained LLM is Secretly an Unsupervised Confidence Calibrator](https://mlsanity.com/paper/2505.16690/) (2025)
- [Calibrating Deep Neural Networks using Explicit Regularisation and Dynamic Data Pruning](https://mlsanity.com/paper/2212.10005/) (2022)
- [Multiclass Confidence and Localization Calibration for Object Detection](https://mlsanity.com/paper/2306.08271/) (2023)
- [Deep Probability Estimation](https://mlsanity.com/paper/2111.10734/) (2021)
- [Dynamic Correlation Learning and Regularization for Multi-Label Confidence Calibration](https://mlsanity.com/paper/2407.06844/) (2024)
- [MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification](https://mlsanity.com/paper/2108.10709/) (2021)
- [Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection](https://mlsanity.com/paper/2303.14404/) (2023)
- [Cal-DETR: Calibrated Detection Transformer](https://mlsanity.com/paper/2311.03570/) (2023)
- [Does deep learning model calibration improve performance in class-imbalanced medical image classification?](https://mlsanity.com/paper/2110.00918/) (2021)
- [Mutation-Based Adversarial Attacks on Neural Text Detectors](https://mlsanity.com/paper/2302.05794/) (2023)
- [Beyond Classification: Definition and Density-based Estimation of Calibration in Object Detection](https://mlsanity.com/paper/2312.06645/) (2023)
- [Multi-Head Multi-Loss Model Calibration](https://mlsanity.com/paper/2303.01099/) (2023)
- [Threshold-Consistent Margin Loss for Open-World Deep Metric Learning](https://mlsanity.com/paper/2307.04047/) (2023)
- [Calibration Attacks: A Comprehensive Study of Adversarial Attacks on Model Confidence](https://mlsanity.com/paper/2401.02718/) (2024)
- [Average Calibration Error: A Differentiable Loss for Improved Reliability in Image Segmentation](https://mlsanity.com/paper/2403.06759/) (2024)
- [Neural Clamping: Joint Input Perturbation and Temperature Scaling for Neural Network Calibration](https://mlsanity.com/paper/2209.11604/) (2022)
- [AUGCAL: Improving Sim2Real Adaptation by Uncertainty Calibration on Augmented Synthetic Images](https://mlsanity.com/paper/2312.06106/) (2023)
- [Unlocking the Potential of Model Calibration in Federated Learning](https://mlsanity.com/paper/2409.04901/) (2024)
- [Average Calibration Losses for Reliable Uncertainty in Medical Image Segmentation](https://mlsanity.com/paper/2506.03942/) (2025)
- [Calibration-Aware Prompt Learning for Medical Vision-Language Models](https://mlsanity.com/paper/2509.15226/) (2025)
- [NCTV: Neural Clamping Toolkit and Visualization for Neural Network Calibration](https://mlsanity.com/paper/2211.16274/) (2022)
- [Towards Reliable Colorectal Cancer Polyps Classification via Vision Based Tactile Sensing and Confidence-Calibrated Neural Networks](https://mlsanity.com/paper/2304.13192/) (2023)
- [OOD-SEG: Exploiting out-of-distribution detection techniques for learning image segmentation from sparse multi-class positive-only annotations](https://mlsanity.com/paper/2411.09553/) (2024)
- [Beta Distribution Learning for Reliable Roadway Crash Risk Assessment](https://mlsanity.com/paper/2511.04886/) (2025)
- [Multi-loss ensemble deep learning for chest X-ray classification](https://mlsanity.com/paper/2109.14433/) (2021)
- [Multiclass Alignment of Confidence and Certainty for Network Calibration](https://mlsanity.com/paper/2309.02636/) (2023)

## Abstract (arXiv)

Recent works have shown that deep neural networks can achieve super-human performance in a wide range of image classification tasks in the medical imaging domain. However, these works have primarily focused on classification accuracy, ignoring the important role of uncertainty quantification. Empirically, neural networks are often miscalibrated and overconfident in their predictions. This miscalibration could be problematic in any automatic decision-making system, but we focus on the medical field in which neural network miscalibration has the potential to lead to significant treatment errors. We propose a novel calibration approach that maintains the overall classification accuracy while significantly improving model calibration. The proposed approach is based on expected calibration error, which is a common metric for quantifying miscalibration. Our approach can be easily integrated into any classification task as an auxiliary loss term, thus not requiring an explicit training round for calibration. We show that our approach reduces calibration error significantly across various architectures and datasets.