  Automatic segmentation of liver tumors in medical images is crucial for the
computer-aided diagnosis and therapy. It is a challenging task, since the
tumors are notoriously small against the background voxels. This paper proposes
a new three-stage curriculum learning approach for training deep networks to
tackle this small object segmentation problem. The learning in the first stage
is performed on the whole input to obtain an initial deep network for tumor
segmenta-tion. Then the second stage of learning focuses the strength-ening of
tumor specific features by continuing training the network on the tumor
patches. Finally, we retrain the net-work on the whole input in the third
stage, in order that the tumor specific features and the global context can be
inte-grated ideally under the segmentation objective. Benefitting from the
proposed learning approach, we only need to em-ploy one single network to
segment the tumors directly. We evaluated our approach on the 2017 MICCAI Liver
Tumor Segmentation challenge dataset. In the experiments, our approach exhibits
significant improvement compared with the commonly used cascaded counterpart.
