论文标题
定量成像原理改善医学图像学习
Quantitative Imaging Principles Improves Medical Image Learning
论文作者
论文摘要
自然图像和医学图像之间的根本差异最近有利于对医学图像应用中的Imagenet转移学习使用自我监督学习（SSL）。图像类型之间的差异主要是由于成像方式和医学图像利用了广泛的基于物理的技术，而自然图像仅使用可见光捕获。尽管许多人证明了医疗图像上的SSL导致了更好的下游任务性能，但我们的工作表明可以获得更多的性能。在构建学习问题时，通常不考虑用于获取医学图像的科学原理。因此，我们建议在生成SSL期间纳入定量成像原理，以提高图像质量和定量生物学准确性。我们表明，该培训模式可为有限数据的下游监督培训提供更好的起始状态。我们的模型还生成了验证临床定量分析软件的图像。
Fundamental differences between natural and medical images have recently favored the use of self-supervised learning (SSL) over ImageNet transfer learning for medical image applications. Differences between image types are primarily due to the imaging modality and medical images utilize a wide range of physics based techniques while natural images are captured using only visible light. While many have demonstrated that SSL on medical images has resulted in better downstream task performance, our work suggests that more performance can be gained. The scientific principles which are used to acquire medical images are not often considered when constructing learning problems. For this reason, we propose incorporating quantitative imaging principles during generative SSL to improve image quality and quantitative biological accuracy. We show that this training schema results in better starting states for downstream supervised training on limited data. Our model also generates images that validate on clinical quantitative analysis software.
