Computationally Efficient Histopathological Image Analysis: Use of GPUs for Classification of Stromal Development Olcay Sertel 1,2 , Antonio Ruiz 3 , Umit Catayurek 1,2 , Manuel Ujaldon 3 , Joel Saltz 1 , Metin Gurcan 1 1 Dept. of Biomedical Informatics, 2 Dept. of Electrical & Computer Engineering, 3 Dept. of Pathology, The Ohio State University, 3 Dept. of Computer Architecture, Dept. of Computer Architecture, The University of Malaga
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Computationally Efficient Histopathological Image Analysis: Use of GPUs for Classification of Stromal Development Olcay Sertel 1,2, Antonio Ruiz 3, Umit.
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Computationally Efficient Histopathological Image Analysis: Use of GPUs for Classification of
Stromal Development
Olcay Sertel1,2, Antonio Ruiz3, Umit Catayurek1,2, Manuel Ujaldon3, Joel Saltz1, Metin Gurcan1
1Dept. of Biomedical Informatics, 2Dept. of Electrical & Computer Engineering, 3Dept. of Pathology, The Ohio State University, 3Dept. of Dept. of
Computer Architecture, Computer Architecture, The University of Malaga
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Why do we need high-performance Why do we need high-performance tools?tools? The size of a single whole-slide image is extremely
large! Typically an uncompressed whole-slide image digitized at
40x is more than 40GB. A spatial resolution of 120K x 120K
120K x 120K x 3 Bytes(RGB) per pixel ≈ 43.2 GB
Complicated and time-consuming image analysis algorithms.
Processing of the whole-slide images is essential to overcome the sampling bias problem.
We need HPC tools that are available due to the huge sizes of whole-slide images and sophisticated image analysis algorithms The processing time can be reduced drastically using different
infrastructures We are investigating novel ways of whole-slide images over
various computational infrastructures Cluster of GPUs
One drawback of GPUs is the low-level programmability Requires good knowledge of architecture Rapid changes in the architecture
However, higher level development tools (CUDA by NVidia)