Feature-Imitating-Networks (FINs) are neural networks with weights that are initialized to approximate closed-form statistical features. In this work, we perform the first-ever evaluation of FINs for biomedical image processing tasks. We begin by training a set of FINs to imitate six common radiomics features, and then compare the performance of networks with and without the FINs for three experimental tasks: COVID-19 detection from CT scans, brain tumor classification from MRI scans, and brain-tumor segmentation from MRI scans; we find that FINs provide best-in-class performance for all three tasks, while converging faster and more consistently when compared to networks with similar or greater representational power. The results of our experiments provide evidence that FINs may provide state-of-the-art performance for a variety of other biomedical image processing tasks.

翻译：特征模仿网络（FINs）是一种神经网络，其权重初始化后可近似闭式统计特征。本研究首次将FINs应用于生物医学图像处理任务进行评估。我们首先训练一组FINs来模仿六种常见放射组学特征，随后针对三项实验任务：CT扫描新冠肺炎检测、MRI扫描脑肿瘤分类及MRI扫描脑肿瘤分割，比较了有无FINs网络的性能。结果表明：FINs在所有三项任务中均达到同类最佳性能，且与具有相似或更强表征能力的网络相比，收敛速度更快、稳定性更高。实验结果证明，FINs或可为其他多种生物医学图像处理任务提供顶尖性能。