Vu Minh Hieu Phan

The work proposes TextCSP, a hierarchical text-guided brain tumor segmentation framework built on the TextBraTS baseline with three components: a text-modulated soft cascade decoder that predicts WT→TC→ET in a coarse-to-fine manner, sub-region-aware prompt tuning that uses learnable soft prompts with a LoRA-adapted BioBERT encoder to generate branch-specialized text representations, and text-semantic channel modulators that convert those representations into channel-wise refinement signals; on the TextBraTS dataset it reaches 87.0% average Dice and 4.81 mm average HD95, improving over the previous best TextBraTS by 1.7% and about 6% (0.32 mm) respectively, with consistent gains across all three sub-regions.

The work proposes TextCSP, a hierarchical text-guided brain tumor segmentation framework built on the TextBraTS baseline with three components: a text-modulated soft cascade decoder that predicts WT→TC→ET in a coarse-to-fine manner, sub-region-aware prompt tuning that uses learnable soft prompts with a LoRA-adapted BioBERT encoder to generate branch-specialized text representations, and text-semantic channel modulators that convert those representations into channel-wise refinement signals; on the TextBraTS dataset it reaches 87.0% average Dice and 4.81 mm average HD95, improving over the previous best TextBraTS by 1.7% and about 6% (0.32 mm) respectively, with consistent gains across all three sub-regions.