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SlimFormer++: Lean 3D Segmentation for CT, MRI and Ultrasound
Most 3D segmentation networks repeat the same block many times across every stage. Each repeat costs parameters, memory and inference time, and few teams check whether each one earns its place. SlimFormer++, from Yang Hong, Lei Zhang, Zhihong Liu and Xujiong Ye at the University of Exeter and Jianqing Mo at Guangdong University of Technology, starts by checking. The authors measure which blocks and which token mixers actually change the features, remove the ones that do not, and end up with a 3D segmenter of 5.98 million parameters that ships as an nnU-Net v2 trainer, with released weights and a 3D Slicer extension. The paper is arXiv:2610.25488.
What SlimFormer++ is
SlimFormer++ is a volumetric segmentation network for CT, MRI and ultrasound. It plugs into nnU-Net v2 as a custom trainer ( nnUNetTrainer_SlimFormerPlusPlus), so you keep nnU-Net's preprocessing, patch sampling, sliding-window inference and folder layout. What changes is the network inside.
The design rests on one question: in a hierarchical encoder, where does token mixing (the part that moves information between voxels, such as attention, Mamba or large convolutions) actually help? The answer the paper reaches is “in the first stage and at the bottleneck, and almost nowhere in between”. The rest of the network…
