We consider calibration of convolutional classifiers for diagnostic decision making. Clinical decision makers can use calibrated classifiers to minimise expected costs given their own cost function. Such functions are usually unknown at training time. If minimising expected costs is the primary aim, algorithms should focus on tuning calibration in regions of probability simplex likely to effect decisions. We give an example, modifying temperature scaling calibration, and demonstrate improved calibration where it matters using convnets trained to classify dermoscopy images.
@article{arxiv.2406.11456,
title = {Calibrating Where It Matters: Constrained Temperature Scaling},
author = {Stephen McKenna and Jacob Carse},
journal= {arXiv preprint arXiv:2406.11456},
year = {2024}
}