医疗报告生成和视觉问题答案的视觉语言模型:一项审查
Iryna Hartsock1, Ghulam Rasool1
1Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States.
Frontiers in artificial intelligence
|December 4, 2024
概括
本综述探讨了医疗视觉语言模型 (VLMs),该模型将计算机视觉和自然语言处理整合到医疗保健中. 它分析了数据集,模型和使用多式联络医疗数据的挑战.
科学领域:
- 人工智能的人工智能
- 生物医学信息学 生物医学信息学
- 医疗成像医学成像
背景情况:
- 医学视觉语言模型 (VLMs) 集成计算机视觉 (CV) 和自然语言处理 (NLP) 来解释组合的视觉和文本医疗数据.
- 变压器架构的进步促进了多式联通数据融合,以实现医疗保健应用中的有效学习.
研究的目的:
- 审查最近在医疗保健特定VLMs的发展.
- 专注于公共可用的医疗报告生成和视觉问题答案 (VQA) 模型.
主要方法:
- 探索18个公共医疗视觉语言数据集.
- 对最近16架医疗VLM的架构和预培训策略进行了深入分析.
- 讨论VLM表现在医疗报告生成和VQA方面的评估指标.
主要成果:
- 确定医疗VLM开发中的关键趋势和方法.
- 现有的公共医疗视觉语言数据集和模型的全面概述.
- 分析当前的挑战,包括数据稀缺,隐私和评估指标.
结论:
- 总结了利用多式联运医疗数据利用VLM来改善医疗保健的进展情况.
- 建议未来的研究方向,以克服医疗VLM开发中发现的障碍.
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