bibtext: @InProceedings{ Sam_LS_MICCAI2024,
                   author = { Sambyal, Abhishek Singh and Niyaz, Usma and Shrivastava, Saksham and Krishnan, Narayanan C. and Bathula, Deepti R. },
                   title = { { LS+: Informed Label Smoothing for Improving Calibration in Medical Image Classification } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15010 },
                   month = {October},
                   pages = { pending },
                }
abstract: Deep Neural Networks (DNNs) exhibit exceptional performance in various tasks; however, their susceptibility to miscalibration poses challenges in healthcare applications, impacting reliability and trustworthiness. Label smoothing, which prefers soft targets based on uniform distribution over labels, is a widely used strategy to improve model calibration. We propose an improved strategy, Label Smoothing Plus (LS+), which uses class-specific prior that is estimated from validation set to account for current model calibration level. We evaluate the effectiveness of our approach by comparing it with state-of-the-art methods on three benchmark medical imaging datasets, using two different architectures and several performance and calibration metrics for the classification task. Experimental results show notable reduction in calibration error metrics with nominal improvement in performance compared to other approaches, suggesting that our proposed method provides more reliable prediction probabilities. Code is available at https://github.com/abhisheksambyal/lsplus.
title: LS+: Informed Label Smoothing for Improving Calibration in Medical Image Classification
authors: Sambyal, Abhishek Singh Niyaz, Usma Shrivastava, Saksham Krishnan, Narayanan C. Bathula, Deepti R.