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![Can a lightweight 3D Vision Transformer outperform heavy SOTA models on volumetric MRI?
In my previous post on SegFormer3D, many researchers and engineers asked how we achieved a 92.3% Dice score while managing the immense memory overhead of volumetric 3D scans (240*240*155 voxels).
The Bottleneck:
Conventional 3D vision transformers like nnFormer or SETR rely on massive parameter footprints (150M – 300M+ parameters), making real-time hospital deployment and fast inference nearly impossible on standard hardware.
My Proposed Architecture:
1. Overlapping 3D Patch Embedding: Captures local volumetric context without losing boundary details.
2. Spatial-Reduction Attention (SRA): Compresses computational complexity using optimized ratios ([4, 4, 1, 1]), preserving multi-scale hierarchical features (F1 to F4).
3. All-MLP Decoder: Eliminates heavy transposed convolutions in favor of ultra-efficient feature aggregation.
The Benchmark:
92.3% Dice Score on BraTS benchmark.
Only ~4.9M parameters— over 30x smaller than nnFormer (~150.5M).
This demonstrates that smart hierarchical attention and lightweight decoders can match and exceed bulky architectures without brute-forcing model capacity.
Check out the benchmark comparison!
#MedicalAI #ComputerVision #DeepLearning #3DSegmentation #BrainTumor BraTS PyTorch Transformers AIHealthcare by @websitewithme](https://scontent-ord5-2.cdninstagram.com/v/t51.82787-15/842422333_18108314036624220_3560578509698356568_n.webp?stp=dst-jpg_e35_s640x640_tt6&_nc_cat=102&ccb=7-5&_nc_sid=18de74&efg=eyJlZmdfdGFnIjoiRkVFRC5iZXN0X2ltYWdlX3VybGdlbi5DMyJ9&_nc_ohc=hX-XGgfG0ygQ7kNvwFCErjp&_nc_oc=AdrG26cCF44f2mjYK_cgqgrBEAg13d_VrP2oBLoCG4rsQERwpsLyAnvPF-xz-oiu6gQNcAuxmL4ByJo-VMJpnRHm&_nc_zt=23&_nc_ht=scontent-ord5-2.cdninstagram.com&_nc_gid=ZTmr88nHQBFrN3y6IuA3mw&_nc_ss=70689&oh=00_AQQAoyJBAIp8mZil1rg5oKpeXGXafSmUVXxPcxqbE07wuw&oe=6AF002F2)
