The goal of the Kaggle* Competition sponsored by Intel and MobileODT* was to use artificial intelligence to improve the precision and accuracy of cervical cancer screening. This case study follows the process used by the third-place winning team, GRXJ. They pooled their respective skill sets to create an algorithm that would improve this life-saving diagnostic procedure.
The aim of the work was to implement, train and evaluate the quality of automated brain tumor multi-label segmentation technique for Magnetic Resonance Imaging based on Tiramisu deep learning architecture.
The University of California, San Francisco targets data-fueled insights for clinical medicine with Intel® Xeon® processors.
One of the Intel® Modern Code Developer Challenge winners, Daniel Falguera, describes many of the optimizations he implemented and why some didn't work.
Veo, GE Healthcare's new CT Scanner reconstruction technology, provides high resolution CT images allowing radiologists to maximize diagnostic accuracy at an optimized low dose to the patient. This paper describes the MBIR optimization steps taken.