50 citations · 121 across the 6 of their papers we have counts for
6 papers · 1 filter
Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images
Bruno Lecouat, Ken Chang, Chuan-Sheng Foo +7
Supervised deep learning algorithms have enabled significant performance gains in medical image classification tasks. But these methods rely on large labeled datasets that require…
Deep feature transfer between localization and segmentation tasks
Szu-Yeu Hu, Andrew Beers, Ken Chang +8
In this paper, we propose a new pre-training scheme for U-net based image segmentation. We first train the encoding arm as a localization network to predict the center of the targe…
DeepNeuro: an open-source deep learning toolbox for neuroimaging
Andrew Beers, James Brown, Ken Chang +4
Translating neural networks from theory to clinical practice has unique challenges, specifically in the field of neuroimaging. In this paper, we present DeepNeuro, a deep learning…
High-resolution medical image synthesis using progressively grown generative adversarial networks
Andrew Beers, James Brown, Ken Chang +4
Generative adversarial networks (GANs) are a class of unsupervised machine learning algorithms that can produce realistic images from randomly-sampled vectors in a multi-dimensiona…
Institutionally Distributed Deep Learning Networks
Ken Chang, Niranjan Balachandar, Carson K Lam +6
Deep learning has become a promising approach for automated medical diagnoses. When medical data samples are limited, collaboration among multiple institutions is necessary to achi…
Sequential 3D U-Nets for Biologically-Informed Brain Tumor Segmentation
Andrew Beers, Ken Chang, James Brown +5
Deep learning has quickly become the weapon of choice for brain lesion segmentation. However, few existing algorithms pre-configure any biological context of their chosen segmentat…