Calibrating the Dice loss to handle neural network overconfidence for biomedical image segmentation

• URL: http://arxiv.org/abs/2111.00528v1
• Date: Sun, 31 Oct 2021 16:02:02 GMT
• Title: Calibrating the Dice loss to handle neural network overconfidence for biomedical image segmentation
• Authors: Michael Yeung, Leonardo Rundo, Yang Nan, Evis Sala, Carola-Bibiane Sch\"onlieb, Guang Yang
• Abstract summary: The Dice similarity coefficient (DSC) is a widely used metric and loss function for biomedical image segmentation. In this study, we identify poor calibration as an emerging challenge of deep learning based biomedical image segmentation. We provide a simple yet effective extension of the DSC loss, named the DSC++ loss, that selectively modulates the penalty associated with overconfident, incorrect predictions.
• Score: 2.6465053740712157
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Dice similarity coefficient (DSC) is both a widely used metric and loss function for biomedical image segmentation due to its robustness to class imbalance. However, it is well known that the DSC loss is poorly calibrated, resulting in overconfident predictions that cannot be usefully interpreted in biomedical and clinical practice. Performance is often the only metric used to evaluate segmentations produced by deep neural networks, and calibration is often neglected. However, calibration is important for translation into biomedical and clinical practice, providing crucial contextual information to model predictions for interpretation by scientists and clinicians. In this study, we identify poor calibration as an emerging challenge of deep learning based biomedical image segmentation. We provide a simple yet effective extension of the DSC loss, named the DSC++ loss, that selectively modulates the penalty associated with overconfident, incorrect predictions. As a standalone loss function, the DSC++ loss achieves significantly improved calibration over the conventional DSC loss across five well-validated open-source biomedical imaging datasets. Similarly, we observe significantly improved when integrating the DSC++ loss into four DSC-based loss functions. Finally, we use softmax thresholding to illustrate that well calibrated outputs enable tailoring of precision-recall bias, an important post-processing technique to adapt the model predictions to suit the biomedical or clinical task. The DSC++ loss overcomes the major limitation of the DSC, providing a suitable loss function for training deep learning segmentation models for use in biomedical and clinical practice.

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