Fangde Liu

TL;DR: This paper provides a deep learning-based strategy for reconstruction of CS-MRI, and bridges a substantial gap between conventional non-learning methods working only on data from a single image, and prior knowledge from large training data sets.

TL;DR: Wang et al. as discussed by the authors proposed a fully automatic method for brain tumor segmentation, which was developed using U-Net based deep convolutional networks, and evaluated on Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, which contain 220 high-grade brain tumor and 54 low-grade tumor cases.

TL;DR: This study proposes a fully automatic method for brain tumor segmentation, which is developed using U-Net based deep convolutional networks, which was evaluated on Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, showing that it can obtain promising segmentation efficiently.

TL;DR: TensorLayer is a Python-based versatile deep learning library that provides high-level modules that abstract sophisticated operations towards neuron layers, network models, training data and dependent training jobs and has transparent module interfaces that allows developers to flexibly embed low-level controls within a backend engine.

TL;DR: This work proposes a conditional Generative Adversarial Networks-based deep learning framework for de-aliasing and reconstructing MRI images from highly undersampled data with great promise to accelerate the data acquisition process and demonstrates that the proposed framework outperforms state-of-the-art CS-MRI methods, in terms of reconstruction error and perceptual image quality.