Confidence calibration on multiclass classification in medical imaging

Current deep learning methods developed to address classification problems related to medical imaging for disease detection and diagnosis are primarily based on binary labels and also with limited focus on confidence calibration. Confidence estimates are closely related to classification accuracy. While existing neural networks have the capability of extending binary labels to multiclass labels, the confidence calibration procedure is generally overlooked. To address the issue, we propose a method called knowledge discriminator risk network (KDR) and a confidence calibration voting algorithm (KDR-CCV) that together enhance classification accuracy, with an emphasis on confidence calibration. Comparative studies on multiclass classification based on the Breast Imaging Reporting and Data Systems (BI-RADS) assessment categories with a dataset containing only binary labels of ultrasound images are conducted. Experimental results show KDR-CCV achieves the overall best classification performance in comparison to other methods that conform to the BI-RADS criterion in addition to the effective improvement on classification accuracy. The proposed method incorporates BI-RADS assessment and artificial intelligence from an application-based broad practice, and can be extended to other medical imaging problems.