Expert knowledge guided manifold representation learning for magnetic resonance imaging-based glioma grading

Radiomics and deep learning have shown high popularity in automatic glioma grading. Radiomics extracts handcrafted features that quantitatively describe the expert knowledge of gliomas, and deep learning is powerful in extracting numerous learned features that facilitate the final classification. However, the performance of existing methods can still be improved as their complementary strengths haven't been sufficiently investigated and integrated. Furthermore, the final prediction at the testing phase normally needs lesion maps which rely too much on the segmentation accuracy and could be troublesome. To address these challenges, we propose an expert knowledge guided manifold representation learning (ENROL) framework for accurate glioma grading without the reliance on segmentation maps during testing. Low-dimensional manifolds of the handcrafted features and learned features are constructed to mine the implicit relationship between radiomics and deep learning, and therefore to dig mutual consent representation for the glioma grades. With a specially designed manifold discrepancy measurement, the grading model can exploit the MRI data and expert knowledge more effectively 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 on both single-sequence and multi-sequence MRI data. Results show that our framework can generate promising glioma grading performance.