Mutation status of isocitrate dehydrogenase (IDH) in gliomas exhibits distinct prognosis. It poses challenges to jointly perform tumor segmentation and gene prediction directly using label-deprived multi-parametric MR images from clinics . We propose a novel multimodal domain adaptive segmentation (MDAS) framework, which derives unsupervised segmentation of tumor foci by learning data distribution between public dataset with labels and label-free targeted dataset. High-level features of radiomics and deep network are combined to manage IDH subtyping. Experiments demonstrate that our method adaptively aligns dataset from both domains with more tolerance toward distribution discrepancy during segmentation procedure and obtains competitive genotype prediction performance.
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