MTCLF: A multitask curriculum learning framework for unbiased glaucoma screenings.

• The prediction bias is mainly caused by data imbalance. • Curriculum coefficients are useful for mining hard samples and improving model generalization. • Teacher-student network ensures unbiased glaucoma diagnoses and evidence map predictions. • The evidence map output by the student network can provide more detailed information. Glaucoma is a disease that causes irreversible damage to the optic nerve. Research on accurate automatic screening algorithms is essential for the prevention and treatment of glaucoma. However, due to the imbalance of existing datasets and the existence of some hard samples that accompany other diverse and complex fundus diseases, the performance of current glaucoma screening algorithms is limited. In addition, the lack of interpretability also makes it difficult for the current algorithms to meet the requirements of clinical applications. In this paper, we propose a new multitask curriculum learning framework (MTCLF) for unbiased glaucoma screenings and visualizations of model decision-making areas. MTCLF is a teacher-student framework. The teacher network is used to generate the label evidence map. The student network can diagnose glaucoma and predict the evidence map at the same time with the well-designed dual-branch CNN structure and collaborative learning module. We design two curriculum coefficients θ and σ to guide the training process of the student network in the sample space so that the student network can adaptively balance the sample contribution, reduce the prediction bias and mine hard samples. The experimental results show that the accuracy, sensitivity, specificity, AUC and F 2 -score of MTCLF based on the LAG dataset for glaucoma diagnoses are 0.967, 0.961, 0.970, 0.996, and 0.958, respectively. These results are superior to those of the state-of-the-art methods. MTCLF not only achieves the best performance for unbiased glaucoma diagnoses but also generates a reliable evidence map to help clinicians explore fine lesion areas. [ABSTRACT FROM AUTHOR]