Qian Wang

Skeletal bone age assessment is a common clinical practice to investigate endocrinology, genetic and growth disorders of children. However, clinical interpretation and bone age analyses are time-consuming, labor intensive and often subject to inter-observer variability.

We propose a generative adversarial approach to address the medical image synthesis problem. Specifically, we train a fully convolutional network (FCN) to generate a target image given a source image. To produce more realistic image, we propose to use adversarial learning strategy to better model the FCN. Moreover, an image-gradient-difference based loss function is proposed to avoid generating blurry images. Also, a long-term residual unit is explored to help the training of the network. We further apply auto-context model to implement a context-aware framework. Experimental results show the robustness and accuracy of our method in synthesizing various medical images.

We propose a novel deformable registration method of using the deep neural network to directly learn the mapping from an image pair to the corresponding deformation field. This highly non-linear mapping is modeled by the novel cue-aware deep regression network, in which we adopt contextual cue to better guide the learning process…