SwitchNet: Adaptive Distribution Switching in UNet for Brain Lesion Segmentation

Automatic brain lesion segmentation enhances diagnostic efficiency by enabling detailed texture analysis and precise delineation of tumor subregions. Multimodal MRI has improved segmentation accuracy by combining complementary information from different modalities. Conventional methods either fuse all modalities uniformly, obscuring how individual modalities contribute to specific segmentation subtasks, or predefine modality-to-subregion mappings based on prior medical knowledge. The former limits interpretability on modality contribution during training, while the latter relies on parameter-heavy architectures like cascaded subnetworks, making models struggle to adapt to varying modalities. To address these challenges, this paper proposes SwitchNet, a novel model that integrates interpretability into the training process and optimizes parameter efficiency without relying on predefined modality selection. First, we propose Adaptive Encoder and Decoder Blocks employing dynamic switching mechanisms to efficiently allocate feature space and prioritize critical subtasks. These blocks enable the model to automatically identify and utilize the most informative modalities. By strategically allocating parameters to modalities, our model optimizes overall parameter complexity while maintaining strong performance. Second, we propose a Guide-Contribution Mechanism to provide interpretability during training by quantitatively revealing the contributions of individual modalities to the segmentation process. This mechanism offers valuable insights into how the model delineates tumor subregions. SwitchNet was validated on three benchmark datasets, including BraTS 2023, ISLES 2022, and UCSF-PDGM, achieving competitive segmentation performance while significantly enhancing interpretability and maintaining parameter efficiency without extra cost. These results highlight its potential for efficient tumor segmentation and clinical explainability.