Leveraging curriculum learning to address out-of-distribution data and inter-observer variability for lung nodule diagnostic interpretation

We propose a novel curriculum learning (CL) approach for lung nodule diagnostic interpretation. Our CL approach embeds an easy-to-moderate-to-hard case strategy into the incremental learning process by starting the learning process using In-Distribution (ID) cases and then adds iteratively easy to moderate to hard cases determined based on their Out-of-Distribution (OoD) score. Using the NIH/NCI Lung Image Database Consortium (LIDC) data, we show that the CL approach improves the classification of the minority class (malignant cases) even for the out-of-distribution (OoD) samples as well as when the inter-observer interpretation variability is high. These results are significant because they show the potential of curriculum learning for improving the performance of Computer-aided Diagnosis (CAD) systems in the presence of unbalanced datasets and class label uncertainty.