通过使用CLIP和视觉变压器进行MRI脑瘤分类的少数拍摄和零拍摄学习.
Abir Das1,2, Saurabh Singh3
1JW Kim College of Future Studies (JCFS), Woosong University, Daejeon 34606, Republic of Korea.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
短时间学习 (FSL) 显著改善了MRI扫描中的脑瘤分类,达到85%的准确性. 这种数据效率高的方法在标记数据稀缺时优于标准方法.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 从MRI中准确地分类脑瘤至关重要,但由于有限的注释数据而受到阻碍.
- 数据高效的学习范式,如少射击学习 (FSL) 和零射击学习 (ZSL) 提供了潜在的解决方案.
研究的目的:
- 用深度学习和视觉语言模型来比较FSL和ZSL用于脑瘤诊断.
- 在严格的标签约束下建立数据效率高的MRI分类的基准.
主要方法:
- 评估了原型网络 (ProtoNet) 与CNN,ResNet-18和视觉变压器的骨干.
- 测试了1000个随机抽样的五次射击,四次发作.
- 与微调的ResNet-50基线和CLIP (ZSL) 模型进行比较.
主要成果:
- 在ResNet-18 ProtoNet中,准确度达到了85%±8% (F1 = 0.85).
- 这超过了ResNet-50基线 (42%±12%) 和CLIP (ZSL) (30%±10%).
- 一个仅视觉ZSL基线实现了54%±11%的准确性.
结论:
- 基于尺度的FSL比MRI分类的标准微调提供了43%的绝对改进.
- FSL为数据效率高的脑瘤诊断提供了强大的基准,标签有限.
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