HOPPR

About HOPPR

HOPPR provides a foundation model API for medical imaging that enables secure data handling and efficient model fine-tuning across various imaging modalities. This platform reduces the time and cost of AI development, facilitating faster prototyping and scalable applications in research and clinical settings.

```xml <problem> Developing AI models for medical imaging is complex and costly, requiring secure data handling, extensive datasets across various modalities, and high-performance computing resources. Navigating data privacy regulations and minimizing the burden of IRB and PHI compliance further complicates the process. </problem> <solution> HOPPR offers a foundation model API designed to streamline AI development in medical imaging. The platform provides secure data handling, model fine-tuning capabilities, and access to diverse datasets across modalities, anatomies, demographics, and geographies. By reducing the time and cost associated with AI development, HOPPR enables faster prototyping, hypothesis testing, and scalable applications for research and clinical settings. The platform also facilitates access to cutting-edge research, industry leaders, and multi-center collaborations. </solution> <features> - Foundation model API for various medical imaging modalities - Secure data handling with anonymization and regulatory compliance - Scalable, high-performance GPU environment for model training - Access to diverse datasets across modalities, anatomies, demographics, and geographies - Streamlined research with minimized IRB and PHI hurdles through access to de-identified data - Integration with HOPPR’s labeling services and external services </features> <target_audience> HOPPR's primary users are researchers and developers in the medical imaging field who seek to accelerate AI model development while maintaining data security and regulatory compliance. </target_audience> ```

What does HOPPR do?

HOPPR provides a foundation model API for medical imaging that enables secure data handling and efficient model fine-tuning across various imaging modalities. This platform reduces the time and cost of AI development, facilitating faster prototyping and scalable applications in research and clinical settings.

Where is HOPPR located?

HOPPR is based in Chicago, United States.

When was HOPPR founded?

HOPPR was founded in 2019.

How much funding has HOPPR raised?

HOPPR has raised 3000000.

Location
Chicago, United States
Founded
2019
Funding
3000000
Employees
32 employees

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HOPPR

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Executive Summary

HOPPR provides a foundation model API for medical imaging that enables secure data handling and efficient model fine-tuning across various imaging modalities. This platform reduces the time and cost of AI development, facilitating faster prototyping and scalable applications in research and clinical settings.

hoppr.ai3K+
Founded 2019Chicago, United States

Funding

$

Estimated Funding

$3M+

Team (30+)

No team information available.

Company Description

Problem

Developing AI models for medical imaging is complex and costly, requiring secure data handling, extensive datasets across various modalities, and high-performance computing resources. Navigating data privacy regulations and minimizing the burden of IRB and PHI compliance further complicates the process.

Solution

HOPPR offers a foundation model API designed to streamline AI development in medical imaging. The platform provides secure data handling, model fine-tuning capabilities, and access to diverse datasets across modalities, anatomies, demographics, and geographies. By reducing the time and cost associated with AI development, HOPPR enables faster prototyping, hypothesis testing, and scalable applications for research and clinical settings. The platform also facilitates access to cutting-edge research, industry leaders, and multi-center collaborations.

Features

Foundation model API for various medical imaging modalities

Secure data handling with anonymization and regulatory compliance

Scalable, high-performance GPU environment for model training

Access to diverse datasets across modalities, anatomies, demographics, and geographies

Streamlined research with minimized IRB and PHI hurdles through access to de-identified data

Integration with HOPPR’s labeling services and external services

Target Audience

HOPPR's primary users are researchers and developers in the medical imaging field who seek to accelerate AI model development while maintaining data security and regulatory compliance.

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