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Found 6 papers, 1 papers with code
Localization of Critical Findings in Chest X-Ray without Local Annotations Using Multi-Instance Learning
The automatic detection of critical findings in chest X-rays (CXR), such as pneumothorax, is important for assisting radiologists in their clinical workflow like triaging time-sensitive cases and screening for incidental findings.
Joint Spatial-Angular Sparse Coding for dMRI with Separable Dictionaries
High angular resolution diffusion imaging (HARDI) can produce better estimates of fiber orientation than the popularly used diffusion tensor imaging, but the high number of samples needed to estimate diffusivity requires longer patient scan times.
(k,q)-Compressed Sensing for dMRI with Joint Spatial-Angular Sparsity Prior
Advanced diffusion magnetic resonance imaging (dMRI) techniques, like diffusion spectrum imaging (DSI) and high angular resolution diffusion imaging (HARDI), remain underutilized compared to diffusion tensor imaging because the scan times needed to produce accurate estimations of fiber orientation are significantly longer.
Global Optimality in Separable Dictionary Learning with Applications to the Analysis of Diffusion MRI
In the classical setting, signals are represented as vectors and the dictionary learning problem is posed as a matrix factorization problem where the data matrix is approximately factorized into a dictionary matrix and a sparse matrix of coefficients.
Deep Learning for Pneumothorax Detection and Localization in Chest Radiographs
Pneumothorax is a critical condition that requires timely communication and immediate action.
Automatic Estimation of Ulcerative Colitis Severity from Endoscopy Videos using Ordinal Multi-Instance Learning
In clinical trials, an endoscopy video is assigned an MES based upon the most severe disease activity observed in the video.
