  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.
