Abstract: Highlights•Glioma segmentation network is designed based on glioma hierarchical structure.•Whole glioma prediction is proposed to reduce wrongly segmented points.•Glioma boundary prediction is introduced to provide semantic glioma contour.•Importance ranking fusion is introduced to reduce feature redundancy.•Our hybrid enhanced-gradient cross-entropy loss can solve class-imbalance problem.
External IDs:dblp:journals/eswa/ZhangZZQWY22
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