A Novel Loss Calibration Strategy for Object Detection Networks Training on Sparsely Annotated Pathological Datasets

Recently, object detection frameworks based on Convolutional Neural Networks (CNNs) have become powerful methods for various tasks of medical image analysis; however, they often struggle with most pathological datasets, which are impossible to annotate all the cells. Obviously, sparse annotations may lead to a seriously miscalculated loss in training, which limits the performance of networks. To address this limitation, we investigate the internal training process of object detection networks. Our core observation is that there is a significant density difference between the regression boxes of the positive instances and negative instances. Our novel Boxes Density Energy (BDE) focuses on utilizing the densities of regression boxes to conduct loss-calibration, which is dedicated to reducing the miscalculated loss, meanwhile to penalizing mispredictions with a relatively more significant loss. Thus BDE can guide networks to be trained along the right direction. Extensive experiments have demonstrated that, BDE on the sparsely annotated pathological dataset can significantly boost the performance of networks, and even with 1.0–1.5% higher recall than networks trained on the fully annotated dataset.