Summary of Hoppr Medical-grade Platform For Medical Imaging Ai, by Kalina P. Slavkova et al.
HOPPR Medical-Grade Platform for Medical Imaging AI
by Kalina P. Slavkova, Melanie Traughber, Oliver Chen, Robert Bakos, Shayna Goldstein, Dan Harms, Bradley J. Erickson, Khan M. Siddiqui
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a solution to address the barriers hindering the deployment of large vision language models (LVLMs) in medical imaging use cases. The authors introduce the HOPPR Medical-Grade Platform, which provides powerful computational infrastructure, foundation models for fine-tuning, and a robust quality management system. The platform leverages millions of imaging studies and text reports to pretrain foundation models and enable use case-specific cohorts for fine-tuning. With its API, developers can securely host models on the platform and make inferences within established clinical workflows. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The HOPPR Medical-Grade Platform helps overcome challenges in deploying LVLMs in medical imaging by offering a powerful computational infrastructure, foundation models for fine-tuning, and a robust quality management system. This solution enables radiologists to optimize their workflows and meet growing demands in the field. |
Keywords
» Artificial intelligence » Fine tuning
