Summary of Honeybee: a Scalable Modular Framework For Creating Multimodal Oncology Datasets with Foundational Embedding Models, by Aakash Tripathi et al.
HoneyBee: A Scalable Modular Framework for Creating Multimodal Oncology Datasets with Foundational Embedding Models
by Aakash Tripathi, Asim Waqas, Matthew B. Schabath, Yasin Yilmaz, Ghulam Rasool
First submitted to arxiv on: 13 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB)
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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 In this paper, researchers introduce HoneyBee, a scalable modular framework for building multimodal oncology datasets that leverages foundation models to generate representative embeddings. The framework integrates various data modalities, including clinical diagnostic and pathology imaging data, medical notes, reports, records, and molecular data. It employs data preprocessing techniques and foundation models to generate embeddings that capture the essential features and relationships within the raw medical data. The generated embeddings are stored in a structured format using Hugging Face datasets and PyTorch dataloaders for accessibility. Vector databases enable efficient querying and retrieval for machine learning applications. The framework is designed to be extensible to other medical domains and aims to accelerate oncology research by providing high-quality, machine learning-ready datasets. The authors demonstrate the effectiveness of HoneyBee through experiments assessing the quality and representativeness of these embeddings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, this paper creates a special tool called HoneyBee that helps doctors and researchers build really big, useful databases for cancer treatment. It combines lots of different types of medical information to create a powerful resource that can help machines learn more about cancer. This can speed up the discovery of new treatments and improve patient care. |
Keywords
» Artificial intelligence » Machine learning
