多路径:适应一个大型多模式模型用于多路径病理报告生成报告
Faruk Ahmed1, Lin Yang1, Tiam Jaroensri1
1Google Research Department, Google, Mountain View, California.
概括
大型多式联络模型具有较长的上下文窗口,现在可以从数千个组织病理学图像补丁中生成诊断. 这种计算病理学的进步有助于生成医疗报告,提高诊断准确度.
科学领域:
- 计算病理学计算病理学
- 人工智能在医学中的应用
- 医学诊断 医学诊断 医学诊断
背景情况:
- 组织病理学病例解释对于医学诊断和治疗至关重要.
- 当前的计算病理学工具在多幻灯片,高放大分析方面遇到了困难.
- 病理学家必须整合每个病例的多个幻灯片的发现.
研究的目的:
- 评估大型多式模式 (LMM) 在从广泛的组织病理学数据中生成诊断的有效性.
- 与传统方法相比,评估人工智能生成的报告的临床准确性和实用性.
主要方法:
- 使用Gemini 1.5 Flash,一个LMM,具有100万个令牌上下文窗口.
- 从多个全幻灯片图像 (WSIs) 处理了多达40,000个图像补丁 (768x768像素),放大10倍.
- 收集了专家病理学家对生成的诊断报告的评估.
主要成果:
- 从大规模的组织病理图像数据集生成底线诊断.
- 在68%的案例中 (最多5个幻灯片),人工智能生成的报告在临床上是准确的,并且比原始报告更受欢迎.
- 性能显示在6个或更多的幻灯片下降,表明有改进的空间.
结论:
- 具有长文本能力的LMM在计算病理学中对复杂的医疗报告生成有显著的前景.
- 这项技术可以帮助从多个WSIs汇总成千上万个图像补丁中的发现.
- 需要进一步的研究,以优化涉及更高数量的幻灯片的情况的性能.
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