Stroma classification for neuroblastoma on graphics processors.
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.
Ruiz
Antonio
A
Computer Architecture Department, University of Málaga, Málaga, Spain. aruiz@ac.uma.es
Sertel
Olcay
O
Ujaldón
Manuel
M
Catalyurek
Umit
U
Saltz
Joel
J
Gurcan
Metin N
MN
D000465:Algorithms; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D009447:Neuroblastoma / Q000473:pathology*; D010363:Pattern Recognition, Automated; D017154:Stromal Cells / Q000145:classification / Q000473:pathology