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.

本文提出一种新的混合阶段的自主课程学习（Mo-SCL）方法，通过集成不确定性和损失来更好地在线估计难度，并将易于和困难样本混合在同一个小批次中以适当地缓解困难样本的问题，在医学图像分析任务中（如通用病变检测）获得了成功。

混合阶 self-paced 策略课程学习用于全局病变检测

Mixed-order self-paced curriculum learning for universal lesion detection

Weakly-supervised object detection (WOD) is a challenging problems in
computer vision. The key problem is to simultaneously infer the exact object
locations in the training images and train the object detectors, given only the
training images with weak image-level labels. Intuitively, by simulating the
selective attention mechanism of human visual system, saliency detection
technique can select attractive objects in scenes and thus is a potential way
to provide useful priors for WOD. However, the way to adopt saliency detection
in WOD is not trivial since the detected saliency region might be possibly
highly ambiguous in complex cases. To this end, this paper first
comprehensively analyzes the challenges in applying saliency detection to WOD.
Then, we make one of the earliest efforts to bridge saliency detection to WOD
via the self-paced curriculum learning, which can guide the learning procedure
to gradually achieve faithful knowledge of multi-class objects from easy to
hard. The experimental results demonstrate that the proposed approach can
successfully bridge saliency detection and WOD tasks and achieve the
state-of-the-art object detection results under the weak supervision.

本文提出一种利用自我学习课程计划的方法，通过显著性检测将其应用于弱监督目标检测 (WOD) 任务中，从而逐渐实现对于从易到难的多类物品的忠实知识，并取得了最先进的弱监督物体检测结果。