基于补丁的特征映射与生成对抗网络用于辅助关节骨折检测
Shang-Lin Chung1, Chi-Tung Cheng2, Chien-Hung Liao2
1Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Computers in biology and medicine
|January 10, 2025
概括
这项研究引入了一种补丁辅助生成对抗网络 (PAGAN),以改善盆腔放射图 (PXR) 中的部骨折检测. 通过专注于骨折区域,PAGAN提高了分类准确性和模型可解释性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机辅助诊断 计算机辅助诊断
背景情况:
- 骨折是一个主要的公共卫生问题,特别是在老年人中.
- 盆腔放射 (PXR) 对于诊断骨折至关重要.
- 对于关节骨折检测的现有分类模型有时缺乏通过专注于非骨折区域的解释性.
研究的目的:
- 通过使用弱监督学习来提高部骨折检测模型的可解释性.
- 为了提高模型对实际骨折区域的关注.
- 引入一种定量方法来评估模型对兴趣区域 (ROI) 的重点.
主要方法:
- 提出了一个补丁辅助生成对抗网络 (PAGAN) 作为分类模型的辅助模块.
- 整合了PAGAN与SOTA模型,如EfficientNetB0,ResNet50和DenseNet121. 这样的模型.
- 使用GradCAM进行注意热图,并计算了IOU和Dise系数的交叉点,以评估模型的解释性.
主要成果:
- 通过PAGAN集成,EfficientNetB0 (93.61%至95.97%),ResNet50 (90.66%至94.89%) 和DenseNet121 (93.51%至94.49%) 的分类准确度得到了提高.
- 用IOU测量的模型可解释性得到显著改进:EfficientNetB0 (0.32至0.54),ResNet50 (0.28至0.40) 和DenseNet121 (0.37至0.51).
结论:
- 拟议的PAGAN模块提高了关节骨折检测模型的性能和可解释性.
- 帕甘有效地将模型的注意力集中在骨折区域,提高了诊断可靠性.
- 这种方法为改善骨科中人工智能驱动的医学图像分析提供了有价值的工具.
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