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EGDCL: An Adaptive Curriculum Learning Framework for Unbiased Glaucoma Diagnosis

Today's computer-aided diagnosis (CAD) model is still far from the clinical practice of glaucoma detection, mainly due to the training bias originating from 1) the normal-abnormal class imbalance and 2) the rare but significant hard samples in fundus images. However, debiasing in CAD is not trivial because existing methods cannot cure the two types of bias to categorize fundus images. In this paper, we propose a novel curriculum learning paradigm (EGDCL) to train an unbiased glaucoma diagnosis model with the adaptive dual-curriculum. Innovatively, the dual-curriculum is designed with the guidance of evidence maps to build a training criterion, which gradually cures the bias in training data. In particular, the dual-curriculum emphasizes unbiased training contributions of data from easy to hard, normal to abnormal, and the dual-curriculum is optimized jointly with model parameters to obtain the optimal solution. In comparison to baselines, EGDCL significantly improves the convergence speed of the training process and obtains the top performance in the test procedure. Experimental results on challenging glaucoma datasets show that our EGDCL delivers unbiased diagnosis (0.9721 of Sensitivity, 0.9707 of Specificity, 0.993 of AUC, 0.966 of F2-score) and outperform the other methods. It endows our EGDCL a great advantage to handle the unbiased CAD in clinical application.

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