| # Code provenance and third-party notices |
|
|
| The standalone model consolidates project SlimFormer components: window |
| partition, MLP/LayerScale, dense window Mamba, aligned MedNeXt decoding, |
| plans-aware downsampling, refined-trunk routing and subpixel upsampling. |
| AugmentedMixer is the project-owner-supplied R5 CapFree implementation. |
| SelectiveMixer is the descriptive name for DenseWindowMamba with the same |
| computation. Encoder2/3 retain MLP residuals but no token-mixer residuals. |
| The release model file matches the currently inspected training source. |
| The Mamba-free implementation (`nnUNetTrainer_SlimFormerPlusPlus_MambaFree.py`) is a |
| copy of that file in which SelectiveMixer is replaced by window self-attention |
| using PyTorch's `scaled_dot_product_attention`; it uses neither Mamba nor |
| causal-conv1d. |
|
|
| The source checkout's Apache-2.0 license text, including its attribution to |
| Division of Medical Image Computing, German Cancer Research Center (DKFZ), |
| Heidelberg, Germany (2019), is preserved verbatim in |
| `licenses/Apache-2.0.txt`. The project research-only license does not replace |
| third-party licenses or withdraw permissions granted by upstream owners. |
| Original project contributions: Copyright (c) 2026 WHHHHHY822. |
|
|
| External implementations remain dependencies; their source is not vendored: |
|
|
| - [PyTorch](https://github.com/pytorch/pytorch) |
| - [MONAI](https://github.com/Project-MONAI/MONAI) |
| - [einops](https://github.com/arogozhnikov/einops) |
| - [Mamba](https://github.com/state-spaces/mamba) |
| - [causal-conv1d](https://github.com/Dao-AILab/causal-conv1d) |
| - [MedNeXt](https://github.com/MIC-DKFZ/MedNeXt), source revision |
| `0b78ed869fbd1cc2fd38754d2f8519f1b72d43ba` |
| - [nnU-Net](https://github.com/MIC-DKFZ/nnUNet) |
| - [dynamic-network-architectures](https://github.com/MIC-DKFZ/dynamic-network-architectures) |
|
|
| Each dependency retains its own license and notices. Consult the exact |
| installed revision for its terms. No dataset or pretrained weights are |
| included in this source release. |
|
|
