  Self-paced curriculum learning (SCL) has demonstrated its great potential in
computer vision, natural language processing, etc. During training, it
implements easy-to-hard sampling based on online estimation of data difficulty.
Most SCL methods commonly adopt a loss-based strategy of estimating data
difficulty and deweighting the `hard' samples in the early training stage.
While achieving success in a variety of applications, SCL stills confront two
challenges in a medical image analysis task, such as universal lesion
detection, featuring insufficient and highly class-imbalanced data: (i) the
loss-based difficulty measurer is inaccurate; ii) the hard samples are
under-utilized from a deweighting mechanism. To overcome these challenges, in
this paper we propose a novel mixed-order self-paced curriculum learning
(Mo-SCL) method. We integrate both uncertainty and loss to better estimate
difficulty online and mix both hard and easy samples in the same mini-batch to
appropriately alleviate the problem of under-utilization of hard samples. We
provide a theoretical investigation of our method in the context of stochastic
gradient descent optimization and extensive experiments based on the DeepLesion
benchmark dataset for universal lesion detection (ULD). When applied to two
state-of-the-art ULD methods, the proposed mixed-order SCL method can provide a
free boost to lesion detection accuracy without extra special network designs.
