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MIL-Adapter:将多个实例学习和视觉语言适配器结合起来,用于几次拍摄的幻灯片级分类
Pablo Meseguer1, Rocío Del Amor2, Valery Naranjo2
1organization=Instituto Universitario de Investigación e Innovación en Tecnología Centrada en el Ser Humano, HUMAN-tech, Universitat Politècnica de València, city=Valencia, country=Spain.
Medical image analysis
|January 30, 2026
概括
MIL-Adapter通过将多个实例学习 (MIL) 与视觉语言模型 (VLMs) 结合起来来增强计算病理学,以准确地进行少数镜头幻灯片分类. 这种方法通过利用文本合体学习来改善癌症诊断,以获得更好的零射击预测.
科学领域:
- 计算病理学计算病理学
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 视觉语言模型 (VLMs) 在视觉表示和零拍摄分类方面表现出色.
- 在计算病理学 (CPath) 中,多个实例学习 (MIL) 对于处理大型全幻灯片图像 (WSIs) 至关重要.
- 现有的VLM缺乏专门的适配器,以应对组织病理幻灯片的复杂性.
研究的目的:
- 在CPath中引入MIL-Adapter,这是一个新的框架,用于在少数拍摄的学习场景中进行一致的幻灯片级分类.
- 为弥补在组织病理学中的VLM专用适配器的缺口.
- 在CPath中使用MIL和适配器技术来提高VLM的性能.
主要方法:
- 开发了MIL-Adapter,将可训练的MIL聚合功能与轻量级视觉语言适配器集成在一起.
- 使用文本合集学习构建歧视性零射击原型.
- 评估了在各种短时间学习配置中对不同数据集的框架.
主要成果:
- 与基线MIL模型相比,MIL-Adapter在数据受限制的设置中表现出更高的性能.
- 该研究验证了文本合体学习对原型构建的有效性.
- 在不同的数据集和少数镜头场景中观察到强大的预测性能.
结论:
- 在计算病理学中,MIL-Adapter为少量学习提供了坚实的基础.
- 该框架成功地将MIL和VLM结合起来,以进行增强的组织病理图像分析.
- 该方法提供了对模型可解释性和在少数镜头设置中的性能有价值的见解.
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