  When dealing with giga-pixel digital pathology in whole-slide imaging, a
notable proportion of data records holds relevance during each analysis
operation. For instance, when deploying an image analysis algorithm on
whole-slide images (WSI), the computational bottleneck often lies in the
input-output (I/O) system. This is particularly notable as patch-level
processing introduces a considerable I/O load onto the computer system.
However, this data management process could be further paralleled, given the
typical independence of patch-level image processes across different patches.
This paper details our endeavors in tackling this data access challenge by
implementing the Adaptable IO System version 2 (ADIOS2). Our focus has been
constructing and releasing a digital pathology-centric pipeline using ADIOS2,
which facilitates streamlined data management across WSIs. Additionally, we've
developed strategies aimed at curtailing data retrieval times. The performance
evaluation encompasses two key scenarios: (1) a pure CPU-based image analysis
scenario ("CPU scenario"), and (2) a GPU-based deep learning framework scenario
("GPU scenario"). Our findings reveal noteworthy outcomes. Under the CPU
scenario, ADIOS2 showcases an impressive two-fold speed-up compared to the
brute-force approach. In the GPU scenario, its performance stands on par with
the cutting-edge GPU I/O acceleration framework, NVIDIA Magnum IO GPU Direct
Storage (GDS). From what we know, this appears to be among the initial
instances, if any, of utilizing ADIOS2 within the field of digital pathology.
The source code has been made publicly available at
https://github.com/hrlblab/adios.

Image and Video Processing;Computer Vision and Pattern Recognition