Won-Ki Jeong

TL;DR: RefineGAN as mentioned in this paper is a variant of fully-residual convolutional autoencoder and generative adversarial networks (GANs) specifically designed for CS-MRI formulation; it employs deeper generator and discriminator networks with cyclic data consistency loss for faithful interpolation in the given under-sampled data.

TL;DR: It is demonstrated that the proposed novel deep learning-based generative adversarial model, RefineGAN, outperforms the state-of-the-art CS-MRI methods by a large margin in terms of both running time and image quality via evaluation using several open-source MRI databases.

TL;DR: This work presents ssEM data for the complete brain of a larval zebrafish at 5.5 days post-fertilization to reduce acquisition time and data management requirements and set the stage for whole-brain structure–function comparisons by co-registering functional reference atlases and in vivo two-photon fluorescence microscopy data from the same specimen.

TL;DR: A deep neural network architecture, FusionNet, is introduced with a focus on its application to accomplish automatic segmentation of neuronal structures in connectomics data, which results in a much deeper network architecture and improves segmentation accuracy.

TL;DR: The proposed method manages the list of active nodes and iteratively updates the solutions on those nodes until they converge and uses only local, synchronous updates and therefore has better cache coherency, is simple to implement, and scales efficiently on parallel architectures.