---
license: cc-by-nc-4.0
tags: [medical-imaging, segmentation, benchmark]
---

# GenSeg-Baselines

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).

This is a **code-only** repository — trained checkpoints and the generated result tables are not hosted here.

**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).

**Datasets:** cvc_clinicdb, kvasir_seg, fives, busi, refuge2, acdc, idridd, pannuke, isic2018, kits19.

**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.

## 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.

## 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`).