Harmony Loss for Unbalanced Prediction

In medical image analysis, in order to reduce the impact of unbalanced data sets on data-driven deep learning models, according to the characteristic that the area under the Precision-Recall curve (AUC_PR) is sensitive to each category of samples, a novel Harmony loss function with fast convergence speed and high stability was constructed. Since AUC_PR needs to be calculated in discrete domain, in order to ensure the continuous differentiability and gradient existence of the Harmony loss, first, the Logistic function was used to approximate the Logical function in AUC_PR. Then, to improve the optimization speed of the Harmony loss during model training, a method of manually setting a certain number of classification thresholds was proposed to further approximate the calculation of AUC_PR. After the above two approximate calculation processes, the Harmony loss with stable gradient and high computational efficiency was designed. In the optimization process of the model, since Harmony loss can reconcile recall and precision of each category under different classification thresholds, thereby, it can not only improve the model's ability to recognize categories with less samples, but also maintain the stability of the training curve. To comprehensively evaluate the effects of Harmony loss function, we performed experiments on image 3D reconstruction, 2D segmentation, and unbalanced classification tasks. Experimental results showed that the Harmony loss achieved the state-of-the-art results on four unbalanced data sets. Moreover, the Harmony loss can be easily combined with existing loss functions, and is suitable for most common deep learning models.