OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models in Medicine

核心概念

The author argues that the development of domain-specific foundation models in medicine can be accelerated by open-sourcing data, algorithms, and pre-trained models through platforms like OpenMEDLab.

摘要

OpenMEDLab is an open-source platform that aims to facilitate the development of multi-modal foundation models in medicine. It provides solutions for training large language and vision models for clinical applications and offers access to pre-trained models for various medical image modalities, clinical text, and protein engineering. The platform encourages researchers to contribute cutting-edge methods and models to advance medical artificial intelligence.
The content discusses the challenges faced in developing foundation models for specific domains like medicine due to a lack of public availability and quality annotations. It highlights the importance of model adaptation techniques and transfer learning from other domains to address these challenges efficiently.
Various projects within OpenMEDLab are introduced, such as RETFound for retinal image analysis, Endo-FM for endoscopic video analysis, MIS-FM for 3D segmentation models, SAM-Med3D for 3D medical images, BROW for whole slide image features, PathoDuet for pathological slide analysis, D-MIM for ultrasound image recognition, USFM for universal ultrasound analysis, and Axon-Seg for neural circuitry profiling.
Evaluation methods like Elo rating tournaments and MedBench are discussed to assess the performance of medical large language models. The content also emphasizes the importance of prompting foundation models in medical image analysis using methods like CITE, MIU-VL, and MedSLAM to improve classification accuracy with minimal annotations.
Overall, OpenMEDLab serves as a valuable resource in advancing research on foundation models in medicine by promoting open-source collaboration among researchers.

統計資料

"It encapsulates not only solutions of pioneering attempts in prompting and fine-tuning large language and vision models."
"Importantly, it opens access to a group of pre-trained foundation models."
"Large-scale datasets are especially scarce and invaluable to make them publicly accessible."
"MedBench introduces a multi-dimensional benchmarking system."
"Ensuring the general efficacy and goodness from medical large language models before real-world deployment is crucial."

引述

"We believe that the OpenMEDLab open-source platform will serve as a distinct resource."
"It provides solutions for training large language and vision models."
"MedBench introduces a multi-dimensional benchmarking system."

從以下內容提煉的關鍵洞見

OpenMEDLab

by Xiaosong Wan... 於 arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.18028.pdf

深入探究

How can open-sourcing data accelerate the development of domain-specific foundation models?

Open-sourcing data plays a crucial role in accelerating the development of domain-specific foundation models by providing researchers with access to large and diverse datasets. Here are some ways it facilitates this acceleration:

Increased Collaboration: Open-sourcing data encourages collaboration among researchers, allowing them to pool resources and expertise to work on common challenges. This collaborative effort can lead to faster progress in developing more robust and accurate foundation models.
Access to Diverse Data: By making datasets openly available, researchers have access to a wider range of data sources, which is essential for training effective domain-specific models. This diversity helps improve model generalization and performance.
Faster Iteration Cycles: With open datasets, researchers can iterate more quickly on model development as they don't need to spend time collecting or curating data themselves. This rapid iteration cycle accelerates the overall research process.
Benchmarking and Evaluation: Open datasets provide a standardized benchmark for evaluating different approaches, enabling fair comparisons between various models developed by different research groups.

How can prompting techniques enhance classification accuracy with minimal annotations?

Prompting techniques are instrumental in enhancing classification accuracy with minimal annotations by leveraging pre-trained language or vision models effectively. Here's how these techniques achieve this:

Knowledge Injection: By designing informative prompts that incorporate relevant information about the task or domain into the input provided to the pre-trained model, prompting techniques guide the model towards making more informed predictions.
Zero-shot Learning: Well-crafted prompts enable zero-shot learning capabilities where a model can make accurate predictions even on classes it has not been explicitly trained on by utilizing prompt guidance effectively.
Few-shot Learning: Prompting allows for few-shot learning scenarios where only a small amount of annotated data is required for fine-tuning or adaptation due to the rich contextual cues provided through prompts.
Domain Adaptation: Prompts tailored specifically for a particular domain help bridge the gap between generic pre-trained models and specific tasks within that domain, leading to improved classification accuracy without extensive annotation requirements.

What challenges do researchers face when developing foundation models in specific domains like medicine?

Developing foundation models in specific domains like medicine presents unique challenges that researchers need to address:

Data Scarcity: Medical datasets are often limited in size due to privacy concerns and regulatory restrictions, making it challenging to train deep learning models effectively.
2Annotation Complexity: Annotating medical images or text requires specialized expertise from healthcare professionals, leading to high annotation costs and time-consuming processes.
3Model Interpretability: Foundation models must be interpretable in medical settings so that clinicians can trust their decisions; achieving both high performance and interpretability poses significant challenges.

These challenges necessitate innovative solutions such as transfer learning paradigms using open-source platforms like OpenMEDLab mentioned earlier in the context above.