Calibrating Where It Matters: Constrained Temperature Scaling
This work addresses calibration for clinical decision makers to minimize expected costs, but it is incremental as it modifies an existing temperature scaling method.
The paper tackles the problem of calibrating convolutional classifiers for diagnostic decision making by focusing on tuning calibration in regions of the probability simplex that affect decisions, and demonstrates improved calibration using convnets on dermoscopy images.
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.