AtlasNet: Multi-atlas Non-linear Deep Networks for Medical Image Segmentation

X-Net+, evaluated using a 10-fold cross validation protocol, provides better results in the ground truth resolution than all the other methods in the state-of- the-art (9

it in their minds during the diagnostic process, we took an image of a healthy person as reference image (such as images of the 1st row in Fig. 1) and the same image with

We show our qualitative results in Figures 4.10 and 4.11. Although, our approach is an unsu- pervised learning method, it can generate the synthetic images which have the same

In this paper, we for- mulate the single image relighting task and propose a novel Deep Relighting Network (DRN) with three parts: 1) scene reconver- sion, which aims to reveal

A series of experiments are designed to verify the anxiolytic effect of ethyl acetate extract and laetispicine in the light/dark test, elevated plus maze test and

Sidib´ e, Ex- ploration of Deep Learning-based Multimodal Fusion for Semantic Road Scene Segmentation, in: VISIGRAPP 2019 - Proceedings of the 14th International Joint Conference

To summarise, our contributions are: (i) the propo- sition of a mathematically well-motivated and computation- ally tractable method to train a constrained deep network, the

In this step, the atlas images and label images are down-sampled and resized to generate low-resolution atlas images I Ai l (i = 1,. , N ) which are then aligned with the