Scaling Resolution of Gigapixel Whole Slide Images Using Spatial Decomposition on Convolutional Neural Networks

Aristeidis Tsaris, Josh Romero, Thorsten Kurth, Jacob Hinkle, Hong Jun Yoon, Feiyi Wang, Sajal Dash, Georgia Tourassi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Gigapixel images are prevalent in scientific domains ranging from remote sensing, and satellite imagery to microscopy, etc. However, training a deep learning model at the natural resolution of those images has been a challenge in terms of both, overcoming the resource limit (e.g. HBM memory constraints), as well as scaling up to a large number of GPUs. In this paper, we trained Residual neural Networks (ResNet) on 22,528 x 22,528-pixel size images using a distributed spatial decomposition method on 2,304 GPUs on the Summit Supercomputer. We applied our method on a Whole Slide Imaging (WSI) dataset from The Cancer Genome Atlas (TCGA) database. WSI images can be in the size of 100,000 x 100,000 pixels or even larger, and in this work we studied the effect of image resolution on a classification task, while achieving state-of-the-art AUC scores. Moreover, our approach doesn't need pixel-level labels, since we're avoiding patching from the WSI images completely, while adding the capability of training arbitrary large-size images. This is achieved through a distributed spatial decomposition method, by leveraging the non-block fat-tree interconnect network of the Summit architecture, which enabled GPU-to-GPU direct communication. Finally, detailed performance analysis results are shown, as well as a comparison with a data-parallel approach when possible.

Original languageEnglish
Title of host publicationProceedings of the Platform for Advanced Scientific Computing Conference, PASC 2023
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9798400701900
DOIs
StatePublished - Jun 26 2023
Event2023 Platform for Advanced Scientific Computing Conference, PASC 2023 - Davos, Switzerland
Duration: Jun 26 2023Jun 28 2023

Publication series

NameProceedings of the Platform for Advanced Scientific Computing Conference, PASC 2023

Conference

Conference2023 Platform for Advanced Scientific Computing Conference, PASC 2023
Country/TerritorySwitzerland
CityDavos
Period06/26/2306/28/23

Funding

This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

  • convolutional neural networks
  • distributed deep learning
  • medical imaging
  • model parallelism
  • spatial decomposition

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