  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 networks,
to derive the "gold standard" information contained in the corresponding
pathological images from CT images. Additionally, we designed a radiological
feature extraction module (RFEM) to directly acquire CT image information and
integrated it with the pathological priors under an effective feature fusion
framework, enabling the entire classification model to generate more indicative
and specific pathologically related features and eventually output more
accurate predictions. The superiority of the proposed model lies in its ability
to self-generate hybrid features that contain multi-modality image information
based on a single-modality input. To evaluate the effectiveness, adaptability,
and generalization ability of our model, we performed extensive experiments on
a large-scale multi-center dataset (i.e., 829 cases from three hospitals) to
compare our model and a series of state-of-the-art (SOTA) classification
models. The experimental results demonstrated the superiority of our model for
lung cancer subtypes classification with significant accuracy improvements in
terms of accuracy (ACC), area under the curve (AUC), and F1 score.
