no code implementations • 12 Nov 2020 • Zhe Xu, Jiangpeng Yan, Jie Luo, William Wells, Xiu Li, Jayender Jagadeesan
The loss function of an unsupervised multimodal image registration framework has two terms, i. e., a metric for similarity measure and regularization.
1 code implementation • 22 Aug 2020 • Geeticka Chauhan, Ruizhi Liao, William Wells, Jacob Andreas, Xin Wang, Seth Berkowitz, Steven Horng, Peter Szolovits, Polina Golland
To take advantage of the rich information present in the radiology reports, we develop a neural network model that is trained on both images and free-text to assess pulmonary edema severity from chest radiographs at inference time.
no code implementations • 27 Feb 2019 • Ruizhi Liao, Jonathan Rubin, Grace Lam, Seth Berkowitz, Sandeep Dalal, William Wells, Steven Horng, Polina Golland
We propose and demonstrate machine learning algorithms to assess the severity of pulmonary edema in chest x-ray images of congestive heart failure patients.
no code implementations • 22 Jun 2018 • Christian Wachinger, Matthew Toews, Georg Langs, William Wells, Polina Golland
We introduce an approach for image segmentation based on sparse correspondences between keypoints in testing and training images.
no code implementations • NeurIPS 2012 • Firdaus Janoos, Weichang Li, Niranjan Subrahmanya, Istvan Morocz, William Wells
Identifying patterns from the neuroimaging recordings of brain activity related to the unobservable psychological or mental state of an individual can be treated as a unsupervised pattern recognition problem.