A Comparative Study of Confidence Calibration in Deep Learning: From Computer Vision to Medical Imaging

Understand

Although deep learning prediction models have been successful in the discrimination of different classes, they can often suffer from poor calibration across challenging domains including healthcare.

• Moreover, the long-tail distribution poses great challenges in deep learning classification problems including clinical disease prediction.
• There are approaches proposed recently to calibrate deep prediction in computer vision, but there are no studies found to demonstrate how the representative models work in different challenging contexts.
• In this paper, we bridge the confidence calibration from computer vision to medical imaging with a comparative study of four high-impact calibration models.