Lisa Tang

TL;DR: A novel segmentation approach based on deep 3D convolutional encoder networks with shortcut connections with results showing that this method performs comparably to the top-ranked state-of-the-art methods, even when only relatively small data sets are available for training.

TL;DR: A novel segmentation approach based on deep convolutional encoder networks and applies it to the segmentation of multiple sclerosis lesions in magnetic resonance images, which eliminates patch selection and redundant calculations at the overlap of neighboring patches and thereby speeds up the training.

TL;DR: 3D image patches are extracted from myelin maps and the corresponding T1-weighted MRIs, and are used to learn a latent joint myelin-T1w feature representation via unsupervised deep learning, suggesting that the proposed method has strong potential for identifying image features that are more sensitive and specific to MS pathology in normal-appearing brain tissues.

TL;DR: The use of deep residual networks on chest CT scans could be an effective case-finding tool for COPD detection and diagnosis, particularly in ex-smokers and current smokers who are being screened for lung cancer.

TL;DR: An algorithm for the automatic generation of large databases of annotated images from a single reference dataset is developed, which uses variational and vibrational spatial deformations, nonlinear radiometric warps mimicking imaging nonhomogeneity, and additive random noise with different underlying distributions.