Abstract: In order to solve the problem of label noise in breast ultrasound image classification, an efficient method called cooperative label correction network (COLC-Net) is proposed. In this method, based on the noise distribution characteristics of the breast ultrasound BI-RADS (breast imaging-reporting and data system) rating, soft labels are proposed for breast ultrasound images, and two networks are proposed for collaborative training. Excellent knowledge is distilled from the two networks to modify the soft labels. With the increase of the accuracy of soft labels, the negative effects of noise labels can be reduced and the learning of clean labels can be enhanced. In order to verify the effectiveness of the method, extensive comparisons are conducted with existing state-of-the-art methods on the dataset. The results demonstrate the effectiveness of the proposed method.