SlimFormer++: Token Mixing Where It Matters for Efficient 3D Medical Image Segmentation

Overview & Synopsis

To determine where computation is needed in volumetric segmentation networks, we analyze block-level redundancy and stage-wise token-mixer contribution. Centered Kernel Alignment reveals high input-output representational similarity within blocks of representative convolutional, hybrid, and Transformer-style segmentation networks. Similarity-guided pruning reduces their parameter counts by 39-75% while decreasing mean Dice by less than 0.4 percentage points. To examine token mixers within blocks, we introduce Anatomical Relation Distortion, which quantifies how bypassing a mixer distorts feature relations between labeled anatomical positions. Applied to an unpruned candidate architecture, Centered Kernel Alignment indicates high representational redundancy in intermediate encoder mixers, whereas Anatomical Relation Distortion shows that they contribute little and the bottleneck mixer most. These findings motivate SlimFormer++, which uses Selective Mixing blocks with windowed Mamba mixing in the first encoder stage and only residual channel MLPs in deeper stages. At the bottleneck, Augmented Mixing injects similarity-weighted sparse long-range context into the axial taps of a depthwise convolution at cost linear in voxel count. Evaluated against 12 baselines across eight public CT, MRI, and ultrasound datasets (1,809 cases), SlimFormer++ achieves the highest mean Dice (87.84%) and lowest mean HD95 (6.50 mm) with the fewest parameters (5.98 million). These results support stage-aware token mixing as a design strategy for improving the accuracy-efficiency trade-off in volumetric segmentation. The source code is available at https://github.com/WHHHHHY822/SlimFormerPlusPlus