# Enabling Next Generation Machine Learning for Large Scale Image Analysis

> **NIH NIH R41** · RNET TECHNOLOGIES, INC. · 2021 · $256,581

## Abstract

Project Summary/Abstract
Deep learning has transformed medical image analysis by delivering clinically meaningful results on challenging
problems like prostate cancer detection and lung screening. In pathology, industry is making significant invest-
ments to develop deep learning tools for diagnostic use in clinical labs. FDA approval of whole-slide digital
pathology images (WSIs) for use in primary diagnosis is further increasing interest, adoption, and investment
in this technology. Judgments made by pathologists are the basis for the treatment of many diseases, yet in-
terobserver variability among pathologists is significant, and errors can lead to overtreatment or even treatment
of healthy patients. Pathology is also facing workforce issues as demand for pathologist services is outpacing
growth of trained pathologists. Computational pathology tools based on deep learning can help address these
problems by providing reproducible diagnoses, performing ”second reads” for human pathologists, automating
tasks to improve pathologist efficiency, and helping general pathologists evaluate challenging cases. GPU accel-
erators have played a significant role in advancing deep learning methods to build computational pathology tools,
with machine learning frameworks (MLFs) like Pytorch and Tensorflow providing researchers with abstractions
to quickly develop models that utilize GPUs. Evolution of GPUs and MLFs has been driven by analysis of small
images, and so these tools cannot be easily applied directly WSIs or other large medical images like three dimen-
sional MRI or CT. Adapting medical imaging problems to small image paradigms supported by GPUs and MLFs
leads to suboptimal performance and increased implementation effort and complexity. More recent approaches
that use streaming or ”unified memory” allow direct analysis of entire WSIs and have demonstrated performance
advantages. These approaches can be slow, complex to implement, and are highly specific to a choice of network
architecture which limits exploration and development of new architectures. More general-purpose, efficient, and
user-friendly frameworks are required to allow the development of WSI scale deep learning.
 This project will develop techniques to automatically map deep learning networks implemented in common
MLF architectures to one or more GPUs for arbitrarily large input images and activation layers. The proposed
software will include a performance modeler to estimate the runtime of a given network on available GPU acceler-
ators. These strategies will enable a new paradigm in deep learning for medical images, allowing the development
of novel networks that are purpose-built for medical applications. Developers will be able to rapidly create and
evaluate these networks using familiar MLF packages. This project will provide approaches to overcome GPU
memory bottlenecks, a scheduler to map the network to available GPUs, integration with common MLFs, and
demonstration using computational...

## Key facts

- **NIH application ID:** 10384903
- **Project number:** 1R41EB032722-01
- **Recipient organization:** RNET TECHNOLOGIES, INC.
- **Principal Investigator:** Gerald Michael Sabin
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $256,581
- **Award type:** 1
- **Project period:** 2021-09-30 → 2022-03-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10384903

## Citation

> US National Institutes of Health, RePORTER application 10384903, Enabling Next Generation Machine Learning for Large Scale Image Analysis (1R41EB032722-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10384903. Licensed CC0.

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