Position-Dependent Urinary Retention in a Traumatic Brain Injury Patient: A Case Report

INTRODUCTION: Voiding disorders are common complication after traumatic brain injury. Usually, they are caused by neurogenic bladder although they can also occur as a result of other pathologic processes and conditions as well as side effects of medications. CASE PRESENTATION: A 62-year-old traumatic brain injury patient with position-dependent urinary retention is presented in this article. Neurogenic bladder with detrusor sphincter dyssynergia was suspected initially, with detection of multiple small bladder stones as the final cause of his urinary retention afterwards. CONCLUSION: Careful clinical, imaging, and urodynamic evaluation must be performed in traumatic brain injury patients to exclude the coexistence of two or more factors leading to urinary dysfunction in this population group

Local Descriptors Optimized for Average Precision

Extraction of local feature descriptors is a vital stage in the solution pipelines for numerous computer vision tasks. Learning-based approaches improve performance in certain tasks, but still cannot replace handcrafted features in general. In this paper, we improve the learning of local feature descriptors by optimizing the performance of descriptor matching, which is a common stage that follows descriptor extraction in local feature based pipelines, and can be formulated as nearest neighbor retrieval. Specifically, we directly optimize a ranking-based retrieval performance metric, Average Precision, using deep neural networks. This general-purpose solution can also be viewed as a listwise learning to rank approach, which is advantageous compared to recent local ranking approaches. On standard benchmarks, descriptors learned with our formulation achieve state-of-the-art results in patch verification, patch retrieval, and image matching.Comment: 13 pages, 8 figures. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201