Dehui Xiang

TL;DR: Experimental results show that the proposed novel Context Pyramid Fusion Network (named CPFNet) is very competitive with other state-of-the-art methods on four different challenging tasks, including skin lesion segmentation, retinal linear lesion segmentsation, multi-class segmentation of thoracic organs at risk and multi- class segmentsation of retinal edema lesions.

TL;DR: In order to accurately detect the liver surface, deformable graph cut was proposed, which effectively integrates the properties and inter-relationship of the input images and initialized surface.

TL;DR: A method to automatically segment the retinal layers in 3-D OCT data with serous retinal pigment epithelial detachments (PED), which is a prominent feature of many chorioretinal disease processes, is proposed.

TL;DR: An end-to-end framework for simultaneous speckle reduction and contrast enhancement for retinal OCT images based on the conditional generative adversarial network (cGAN), with edge loss function added to the final objective so that the model is sensitive to the edge-related details.

TL;DR: A surrogate-assisted classification method to classify retinal OCT images automatically based on convolutional neural networks (CNNs) that has been evaluated on different databases and shows that the proposed method is a very promising tool for classifying the retinal Oct images automatically.