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| license: cc-by-nc-4.0 |
| tags: [medical-imaging, segmentation, benchmark] |
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| # GenSeg-Baselines |
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| Reproducible **code** for a 2D medical-image segmentation benchmark: **8 methods × 10 datasets × 3 seeds/folds, 7 metrics**, evaluated under a **unified resolution-fair protocol**. Companion to the [GenSegDataset](https://huggingface.co/datasets/MaybeRichard/GenSegDataset). |
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| This is a **code-only** repository — trained checkpoints and the generated result tables are not hosted here. |
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| **Methods:** UNet, UNet++, DeepLabV3+ (ResNet-50/ImageNet), Attention-UNet (from scratch), |
| TransUNet (R50-ViT-B/16, input 256), Swin-UNet (Swin-Tiny, input 224), nnU-Net v2 (250 ep), U-Mamba (UMambaBot, 100 ep). |
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| **Datasets:** cvc_clinicdb, kvasir_seg, fives, busi, refuge2, acdc, idridd, pannuke, isic2018, kits19. |
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| **Metrics (computed per image, then aggregated):** Dice, IoU, HD95, ASSD, Sensitivity, Specificity, Precision — |
| plus per-class Dice for the multi-class datasets and paired-Wilcoxon significance on per-image Dice. |
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| ## Resolution-fair protocol |
| Convolutional nets are trained at 512; the fixed-input transformers (Swin-UNet 224, TransUNet 256) and |
| nnU-Net / U-Mamba run at their native size; **every prediction and ground truth is resized to a common |
| 512×512 before scoring**, so boundary metrics (HD95/ASSD, in pixels) are directly comparable across methods. |
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| ## Layout (code only) |
| - `code/framework/` — training/evaluation framework: `train.py`, `test.py`, `eval_at_res.py`, |
| `nnunet_eval.py`; `metrics/` (the 7 metrics + boundary distances); `models/` (SMP wrappers, |
| Attention-UNet, Swin/TransUNet wrappers, model registry); `report/aggregate.py` builds the summary |
| tables (per-dataset Dice/HD95/IoU, per-class Dice, Sensitivity/Precision, significance). |
| - `code/sota/{Swin-Unet,TransUNet}/` — upstream network definitions imported by the Swin-UNet / TransUNet wrappers. |
| - `code/scripts/` — reproduction scripts (unified-512 training & evaluation, nnU-Net / U-Mamba pipelines). |
| - `code/envs/` — conda environments (`seggen.yml`, `nnunet.yml`, `umamba.yml`). |
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