Feature-aware unsupervised lesion segmentation for brain tumor images using fast data density functional transform.

ABSTRACT: We demonstrate that isomorphically mapping gray-level medical image matrices onto energy spaces underlying the framework of fast data density functional transform (fDDFT) can achieve the unsupervised recognition of lesion morphology. By introducing the architecture of geometric deep learning and metrics of graph neural networks, gridized density functionals of the fDDFT establish an unsupervised feature-aware mechanism with global convolutional kernels to extract the most likely lesion boundaries and produce lesion segmentation. An AutoEncoder-assisted module reduces the computational complexity from [Formula: see text] to [Formula: see text], thus efficiently speeding up global convolutional operations. We validate their performance utilizing various open-access datasets and discuss limit

SUBMITTER: Huang SJ 

PROVIDER: S-EPMC10442428 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature