Parameter-Efficient Multimodal Brain Tumor Segmentation With Stage-Wise Shared Adapters

Abstract

Different MRI modalities reveal different parts of a brain tumor. Segmentation models combine information from these modalities to outline tumor regions. Many models use a separate encoder for each modality. However, exchanging features among these encoders at several stages can increase model complexity. We introduce the Block-Wise Shared Adapter (BWSA), which combines compressed features from the modality encoders in a shared path and returns adapted features to each stream. In its fixed-blending form, BWSA adds the adapted features to the original features. Its trainable-gated variant (TG-BWSA) instead learns separate weights for the original and adapted features at each modality and encoder stage. We evaluate both variants on BraTS 2020 and BraTS 2023 with four segmentation backbones. On the official BraTS 2023 online validation set, BWSA increases average region-wise Dice by 1.3--7.6 percentage points relative to the corresponding unmodified backbones. TG-BWSA increases it by 2.8--7.7 points. Both variants add approximately 0.85--3.73% parameters and reduce average HD95 relative to the unmodified backbones.