Many real-world applications have to tackle the Positive-Unlabeled (PU) learning problem,i.e.,learning binary classifiers from a large amountof unlabeled data and a few labeled positive examples. While current state-of-the-art methodsemploy importance reweighting to design various risk estimators, they ignored the learning capability of the model itself, which could haveprovided reliable supervision. This motivatesus to propose a novel Self-PU learning framework, which seamlessly integrates PU learningand self-training. Self-PU highlights three “self”-oriented building blocks: a self-paced training algorithm that adaptively discovers and augments confident positive/negative examples as the training proceeds; a self-calibrated instance-aware loss; and a self-distillation scheme that introduces teacher-students learning as an effectiveregularization for PU learning. We demonstratethe state-of-the-art performance of Self-PU oncommon PU learning benchmarks (MNIST and CIFAR-10), which compare favorably against thelatest competitors. Moreover, we study a real-world application of PU learning,i.e., classifying brain images of Alzheimer’s Disease. Self-PU obtains significantly improved results on the renowned Alzheimer’s Disease Neuroimaging Initiative (ADNI) database over existing methods.