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

用于提高病理图像机器学习系统的成本、可扩展性和可访问性的云策略

基本信息

批准号：
10824959
负责人：
Lee Cooper
金额：
$ 34.71万
依托单位：
NORTHWESTERN UNIVERSITY AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10824959
关键词：
Acceleration Active Learning Address Adoption Attention Benchmarking Biomedical Research Classification Client Clinical Research Cloud Computing Cloud Service Communities Complex Computer software Cost Savings Costs and Benefits Data Data Scientist Data Set Data Storage and Retrieval Development Equilibrium Face Familiarity Funding Goals Growth Human Image Institution Label Machine Learning Maps Memory Methodology Methods Modeling Movement Pathology Pattern Performance Price Publishing Pythons Recipe Recommendation Research Institute Research Personnel Sampling Savings Services Specific qualifier value Structure System Techniques Technology TensorFlow Testing Training Triton Visualization Work algorithm training classification algorithm cloud platform computing resources cost cost effective design digital pathology heuristics improved large datasets locust machine learning algorithm machine learning framework machine learning model migration open source operation pathology imaging service providers software development tool web services whole slide imaging

项目摘要

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.

项目总结