# Cloud strategies for improving cost, scalability, and accessibility of a machine learning system for pathology images

> **NIH NIH R01** · NORTHWESTERN UNIVERSITY · 2023 · $347,144

## Abstract

PROJECT SUMMARY
Machine learning (ML) has seen tremendous advances in the past decade, fueled by growth in computing
power and the availability of large labeled datasets. While the impact of these advances on clinical and
biomedical research is potentially significant, these domains face unique challenges due to the difficulty in
acquiring labels from experts. This proposal will develop new methodology and open-source software that
biomedical data scientists can use with their applications to 1. Improve data labeling by identifying the best
samples for labeling that provide the most benefit for training ML algorithms; 2. Improve generalization of ML
models across institutes; and 3. Perform this work on Amazon Web Services. These methods and software will
be developed in digital pathology applications using multi-institutional datasets. This supplemental funding will
enhance cloud support beyond the original proposal, leveraging recent advances in inference server
technology that can accelerate our software by orders of magnitude, providing significant savings to users. The
supplemental funding will support the additional implementation that is required to incorporate this technology
into our software and will help us benchmark the cost-to-benefit ratio of different compute and storage asset
classes on Amazon Web Services, Microsoft Azure, and Google Cloud Platform. The deliverables from this
work include enhanced software that can support inference serving on multiple cloud service providers and
best practices and recommendations for our users.

## Key facts

- **NIH application ID:** 10824959
- **Project number:** 3R01LM013523-03S1
- **Recipient organization:** NORTHWESTERN UNIVERSITY
- **Principal Investigator:** Lee Cooper
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $347,144
- **Award type:** 3
- **Project period:** 2021-09-01 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10824959

## Citation

> US National Institutes of Health, RePORTER application 10824959, Cloud strategies for improving cost, scalability, and accessibility of a machine learning system for pathology images (3R01LM013523-03S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10824959. Licensed CC0.

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