  Radiomics and deep learning have shown high popularity in automatic glioma
grading. Radiomics can extract hand-crafted features that quantitatively
describe the expert knowledge of glioma grades, and deep learning is powerful
in extracting a large number of high-throughput features that facilitate the
final classification. However, the performance of existing methods can still be
improved as their complementary strengths have not been sufficiently
investigated and integrated. Furthermore, lesion maps are usually needed for
the final prediction at the testing phase, which is very troublesome. In this
paper, we propose an expert knowledge-guided geometric representation learning
(ENROL) framework . Geometric manifolds of hand-crafted features and learned
features are constructed to mine the implicit relationship between deep
learning and radiomics, and therefore to dig mutual consent and essential
representation for the glioma grades. With a specially designed manifold
discrepancy measurement, the grading model can exploit the input image data and
expert knowledge more effectively in the training phase and get rid of the
requirement of lesion segmentation maps at the testing phase. The proposed
framework is flexible regarding deep learning architectures to be utilized.
Three different architectures have been evaluated and five models have been
compared, which show that our framework can always generate promising results.
