Glioma Multimodal MRI Analysis System for Tumor Layered Diagnosis via Multi-task Semi-supervised Learning
Journal:
arXiv
Published Date:
Jan 29, 2025
Abstract
Gliomas are the most common primary tumors of the central nervous system.
Multimodal MRI is widely used for the preliminary screening of gliomas and
plays a crucial role in auxiliary diagnosis, therapeutic efficacy, and
prognostic evaluation. Currently, the computer-aided diagnostic studies of
gliomas using MRI have focused on independent analysis events such as tumor
segmentation, grading, and radiogenomic classification, without studying
inter-dependencies among these events. In this study, we propose a Glioma
Multimodal MRI Analysis System (GMMAS) that utilizes a deep learning network
for processing multiple events simultaneously, leveraging their
inter-dependencies through an uncertainty-based multi-task learning
architecture and synchronously outputting tumor region segmentation, glioma
histological subtype, IDH mutation genotype, and 1p/19q chromosome disorder
status. Compared with the reported single-task analysis models, GMMAS improves
the precision across tumor layered diagnostic tasks. Additionally, we have
employed a two-stage semi-supervised learning method, enhancing model
performance by fully exploiting both labeled and unlabeled MRI samples.
Further, by utilizing an adaptation module based on knowledge self-distillation
and contrastive learning for cross-modal feature extraction, GMMAS exhibited
robustness in situations of modality absence and revealed the differing
significance of each MRI modal. Finally, based on the analysis outputs of the
GMMAS, we created a visual and user-friendly platform for doctors and patients,
introducing GMMAS-GPT to generate personalized prognosis evaluations and
suggestions.
