将语言整合到医学视觉识别和推理中:一项调查
1The School of Computer Science and Technology, East China Normal University, Shanghai 200062, China.
Medical image analysis
|March 2, 2025
概括
视觉语言模型 (VLMs) 集成视觉和文本数据用于医学成像分析. 本调查探讨了医疗VLM,强调了它们通过利用医生从报告中的知识来提高诊断准确性的潜力.
科学领域:
- 人工智能的人工智能
- 医学成像分析 医学成像分析
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
背景情况:
- 视觉语言模型 (VLMs) 弥合了视觉感知和文本解释.
- 医学成像通常包括广泛的文本报告,提供有价值的背景.
- 整合文本信息可以显著改善医学中AI模型的视觉理解.
研究的目的:
- 为医疗视觉语言模型 (VLMs) 提供全面的审查.
- 探索如何在医学视觉任务中利用语言.
- 讨论医疗VLM的潜力和挑战,以进行识别和推理.
主要方法:
- 对医疗VLMs现有文献的审查.
- 分析如何将语言信息集成到视觉任务中.
- 对医学VLM研究的公开可用的数据集的调查.
主要成果:
- 医疗VLM通过将医疗报告中的文本数据纳入视觉理解来提高视觉理解的前景.
- 在医学视觉任务中利用语言存在各种方法.
- 该领域正在迅速发展,越来越多的兴趣和潜在的应用.
结论:
- 医疗VLM代表了医疗保健人工智能的重大进步.
- 对医疗VLM的进一步研究至关重要,以释放其在诊断和治疗方面的全部潜力.
- 这项调查为了解医疗VLMs的当前景观和未来方向提供了基础.
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