Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Gyeonggi-Do, Republic of Korea.
Department of Artificial Intelligence, Tashkent State University of Economics, Tashkent 100066, Uzbekistan.
Sensors (Basel). 2023 Mar 24;23(7):3440. doi: 10.3390/s23073440.
In recent years, considerable work has been conducted on the development of synthetic medical images, but there are no satisfactory methods for evaluating their medical suitability. Existing methods mainly evaluate the quality of noise in the images, and the similarity of the images to the real images used to generate them. For this purpose, they use feature maps of images extracted in different ways or distribution of images set. Then, the proximity of synthetic images to the real set is evaluated using different distance metrics. However, it is not possible to determine whether only one synthetic image was generated repeatedly, or whether the synthetic set exactly repeats the training set. In addition, most evolution metrics take a lot of time to calculate. Taking these issues into account, we have proposed a method that can quantitatively and qualitatively evaluate synthetic images. This method is a combination of two methods, namely, FMD and CNN-based evaluation methods. The estimation methods were compared with the FID method, and it was found that the FMD method has a great advantage in terms of speed, while the CNN method has the ability to estimate more accurately. To evaluate the reliability of the methods, a dataset of different real images was checked.
近年来，人们在合成医学图像的开发方面做了大量工作，但仍缺乏评估其医学适用性的满意方法。现有的方法主要评估图像中的噪声质量以及图像与用于生成它们的真实图像的相似性。为此，他们使用以不同方式提取的图像特征图或图像集分布。然后，使用不同的距离度量来评估合成图像与真实集合的接近程度。但是，无法确定是否仅重复生成了一张合成图像，或者合成集合是否完全重复了训练集。此外，大多数进化度量需要花费大量时间来计算。考虑到这些问题，我们提出了一种可以对合成图像进行定量和定性评估的方法。该方法是两种方法的组合，即基于 FMD 和 CNN 的评估方法。对估计方法与 FID 方法进行了比较，发现 FMD 方法在速度方面具有很大的优势，而 CNN 方法具有更准确的估计能力。为了评估方法的可靠性，我们检查了不同真实图像的数据集。
