在胸部放射图中检测肺结节:使用纯合成数据集进行有效的网络培训的新型成本函数
Shouhei Hanaoka1, Yukihiro Nomura2,3, Takeharu Yoshikawa3
1Department of Radiology, University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan. hanaoka-tky@g.ecc.u-tokyo.ac.jp.
概括
这项研究引入了一个新的成本函数来训练人工智能以使用合成图像在X射线中检测小肺结节. 该方法提高了检测准确度,在真实临床数据上表现优于现有的方法.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 大型放射数据集缺乏足够的小,难以检测的肺结节来训练人工智能.
- 人工结节合成通过将病变嵌入到健康图像中提供了一个解决方案.
- 现有的方法不能有效地利用正负图像配对进行训练.
研究的目的:
- 开发和评估基于培训细分网络的新型成本函数.
- 用合成数据改进难以检测的肺结节的检测.
- 为人工智能培训有效利用正负图像对.
主要方法:
- 一个经过修改的U-Net架构,将新的术语纳入 Dice 损失.
- 在图像对中包含患病区域的对比学习.
- 在131,072张合成肺癌图像和真实胸部X射线上进行培训和评估.
主要成果:
- 与RetinaNet和SSD相比,拟议的方法显示出更高的性能.
- 获得了0.688 (微调) 和0.507 (无微调) 的灵敏度,每图像0.2个假阳性.
- 在对合成图像进行培训后,成功对真实临床数据集进行评估.
结论:
- 这是第一个使用真实临床数据集对合成图像进行训练的肺结节检测方法进行评估的研究.
- 新型成本函数有效地训练人工智能用于结节检测.
- 合成的数据集是公开可用的研究.
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