Classification of lung cancer subtypes on CT images with synthetic pathological priors. (arXiv:2308.04663v1 [eess.IV])

The accurate diagnosis on pathological subtypes for lung cancer is of
significant importance for the follow-up treatments and prognosis managements.
In this paper, we propose self-generating hybrid feature network (SGHF-Net) for
accurately classifying lung cancer subtypes on computed tomography (CT) images.
Inspired by studies stating that cross-scale associations exist in the image
patterns between the same case's CT images and its pathological images, we
innovatively developed a pathological feature synthetic module (PFSM), which
quantitatively maps cross-modality associations through deep neural …

arxiv cancer classification diagnosis feature hybrid images importance lung cancer network paper studies synthetic