Tai Yen-Ling, Huang Shin-Jhe, Chen Chien-Chang, Lu Henry Horng-Shing
Bio-Microsystems Integration Laboratory, Department of Biomedical Sciences and Engineering, National Central University, Taoyuan City 32001, Taiwan.
Chronic Disease Research Center, National Central University, Taoyuan City 32001, Taiwan.
Entropy (Basel). 2021 Feb 11;23(2):223. doi: 10.3390/e23020223.
Nowadays, deep learning methods with high structural complexity and flexibility inevitably lean on the computational capability of the hardware. A platform with high-performance GPUs and large amounts of memory could support neural networks having large numbers of layers and kernels. However, naively pursuing high-cost hardware would probably drag the technical development of deep learning methods. In the article, we thus establish a new preprocessing method to reduce the computational complexity of the neural networks. Inspired by the band theory of solids in physics, we map the image space into a noninteraction physical system isomorphically and then treat image voxels as particle-like clusters. Then, we reconstruct the Fermi-Dirac distribution to be a correction function for the normalization of the voxel intensity and as a filter of insignificant cluster components. The filtered clusters at the circumstance can delineate the morphological heterogeneity of the image voxels. We used the BraTS 2019 datasets and the dimensional fusion U-net for the algorithmic validation, and the proposed Fermi-Dirac correction function exhibited comparable performance to other employed preprocessing methods. By comparing to the conventional z-score normalization function and the Gamma correction function, the proposed algorithm can save at least 38% of computational time cost under a low-cost hardware architecture. Even though the correction function of global histogram equalization has the lowest computational time among the employed correction functions, the proposed Fermi-Dirac correction function exhibits better capabilities of image augmentation and segmentation.
如今，具有高结构复杂性和灵活性的深度学习方法不可避免地依赖于硬件的计算能力。一个拥有高性能图形处理器（GPU）和大量内存的平台能够支持具有大量层和内核的神经网络。然而，盲目追求高成本硬件可能会拖累深度学习方法的技术发展。因此，在本文中，我们建立了一种新的预处理方法来降低神经网络的计算复杂度。受物理学中固体能带理论的启发，我们将图像空间同构地映射到一个非相互作用物理系统，然后将图像体素视为类粒子簇。接着，我们将费米 - 狄拉克分布重构为体素强度归一化的校正函数以及无意义簇分量的滤波器。在这种情况下，经过滤波的簇能够描绘出图像体素的形态异质性。我们使用BraTS 2019数据集和维度融合U型网络进行算法验证，所提出的费米 - 狄拉克校正函数表现出与其他采用的预处理方法相当的性能。与传统的z分数归一化函数和伽马校正函数相比，在低成本硬件架构下，所提出的算法可节省至少38%的计算时间成本。尽管全局直方图均衡化的校正函数在所采用的校正函数中计算时间最短，但所提出的费米 - 狄拉克校正函数在图像增强和分割方面表现出更好的能力。
