ProtoRadNet:用于放射学图像分类网络的卷积特征的原型补丁
Prateek Sarangi1, Riya Agarwal2, Tanmay Basu1
1Department of Data Science and Engineering, Indian Institute of Science Education and Research, Bhopal, Bhopal Bypass Road, Bhauri, Bhopal, 462066, Madhya Pradesh, India.
Artificial intelligence in medicine
|December 5, 2025
概括
通过使卷积神经网络 (CNN) 的决定可解释,ProtoRadNet增强了放射学图像分类. 这种新的方法使用原型补丁用于医疗成像中的透明,可靠的AI.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 计算机视觉 计算机视觉
背景情况:
- 卷积神经网络 (CNN) 擅长放射学图像分类,但经常充当"黑子",阻碍医疗专业人员的信任和采用.
- 现有的基于补丁的可解释性原型网络在放射学领域的应用有限.
研究的目的:
- 引入ProtoRadNet,这是一个用于放射学图像分类的新型网络,通过可视化识别的原型来提高可解释性.
- 通过专注于课堂内和课堂间的重要原型来完善CNN培训,而不是不分青红白地处理所有卷积特征.
主要方法:
- ProtoRadNet集成了类间和类内原型,以实现本地化和全球化的可解释性.
- 该模型利用图像级的基本真相,使其适合于现实世界的应用,而详细的注释很少.
主要成果:
- 与最先进的方法相比,ProtoRadNet在脑MRI,胸部CT和MIMIC CXR-LT数据集上表现出卓越的性能.
- 实现了92.16%,96.14%和29.32%的宏观平均F1分数,与竞争方法相比显著改善.
结论:
- ProtoRadNet有效地弥合了CNN发现和医学成像专家理解之间的差距.
- 该模型为分类决策提供了透明的推理,增强了AI在放射学中的实际实用性.
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