title: Stroma classification for neuroblastoma on graphics processors.
abstract: Neuroblastoma is one of the most common childhood cancers. We are developing an image analysis system to assist pathologists in their prognosis. Since this system operates on relatively large-scale images and requires sophisticated algorithms, computerised analysis takes a long time to execute. In this paper, we propose a novel approach to benefit from high memory bandwidth and strong floating-point capabilities of graphics processing units. The proposed approach achieves a promising classification accuracy of 99.4% and an execution performance with a gain factor up to 45 times compared to hand-optimised C++ code running on the CPU.
authors.affiliation: Computer Architecture Department, University of Málaga, Málaga, Spain. aruiz@ac.uma.es
authors.forename: Antonio
authors.initials: A
authors.lastname: Ruiz
authors.forename: Olcay
authors.initials: O
authors.lastname: Sertel
authors.forename: Manuel
authors.initials: M
authors.lastname: Ujaldón
authors.forename: Umit
authors.initials: U
authors.lastname: Catalyurek
authors.forename: Joel
authors.initials: J
authors.lastname: Saltz
authors.forename: Metin N
authors.initials: MN
authors.lastname: Gurcan
mesh_terms: D000465:Algorithms; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D009447:Neuroblastoma / Q000473:pathology*; D010363:Pattern Recognition, Automated; D017154:Stromal Cells / Q000145:classification / Q000473:pathology