Adaptive Elastic Loss Based On Progressive Inter-Class Association For Cervical Histology Image Segmentation

Cervical cancer is one of the most commonly diagnosed cancer types worldwide, while is curable if detected early. However, few computer-aided algorithms have been explored on cervical histology image, which is vital for abnormality assessment. In this paper, an end-to-end deep segmentation network for complex cervical histology images is proposed, and a benchmark evaluation is contributed. Specifically, we observe that four-category cervical histology images possess a progressive inter-class association. To model the relationship, inspired by the elasticity, an adaptive elastic loss is proposed to reduce the deviation between difficult samples and their true categories. Moreover, five evaluation metrics are designed to measure the segmentation performance, and the Window Precision is particularly valuable for the evaluation of semi-supervised algorithms due to its robustness to the mislabeling. Finally, on a cervical histology dataset, benchmark experiments based on deep networks are conducted, and the results demonstrate the superiority of our new loss.