README.md

---
license: other
license_name: mixed-dataset-terms
license_link: LICENSE
task_categories:
  - image-classification
  - image-segmentation
  - object-detection
language:
  - en
tags:
  - medical-imaging
  - wound-care
  - benchmark
  - chronic-wounds
  - multi-task
  - mlhc-2026
pretty_name: WILLIE Wound Benchmark
size_categories:
  - 1K<n<10K
extra_gated_prompt: >-
  This benchmark aggregates three third-party wound datasets, each governed by
  its own terms. By requesting access you confirm you have obtained, or are
  eligible to obtain, the underlying datasets under their original licences —
  including the FUSeg challenge data-use agreement — and that you will use them
  for research purposes only.
extra_gated_fields:
  Name: text
  Institution: text
  Intended use: text
  I agree to the terms of the underlying datasets: checkbox
---

# WILLIE Wound Benchmark

<div align="center">

<img src="assets/hero.png" alt="WILLIE benchmark overview" width="90%">

<em>Three public wound datasets unified into a single 5-class taxonomy with
fixed splits for classification, segmentation and localization.</em>

</div>

The benchmark accompanying **WILLIE**, published at **MLHC 2026**.

Developed in the **Qian Group**, University of Houston.

- **Models:** [QianGroup/willie-weights](https://huggingface.co/QianGroup/willie-weights)
- **Code and notebooks:** [GitHub repository](https://github.com/<GITHUB_ORG>/<REPO>)
- **Paper:** MLHC 2026 *(link to follow)*

---

## What this is

Three public wound datasets — FUSeg, AZH and Medetec — mapped onto one 5-class
taxonomy with fixed, verified splits and a shared evaluation protocol across
three tasks. Before this, the three were benchmarked separately with
incompatible label schemes, which made cross-dataset comparison impossible.

**The contribution is the unification and the splits, not the images.**

| Class | Label |
|:--|--:|
| `diabetic` | 0 |
| `pressure` | 1 |
| `surgical` | 2 |
| `venous` | 3 |
| `no_wound` | 4 |

**3,535 referenced files** across classification, detection and segmentation
tasks.

---

## Contents

```
data.tar.gz              images from all three source datasets
index/
  data_index.csv         unified index, 2,525 rows, the authoritative source
  splits.json            split definitions
  label_map.json         taxonomy mapping
  azh_clean_trainval.csv AZH cleaned train/val index
  fuseg_det_boxes.csv    FUSeg detection boxes
manifests/
  cls_train.csv          918   classification
  cls_val.csv            648
  cls_test.csv           234
  det_train.csv          810   detection
  det_val.csv            400
  fuseg_test.csv         200   segmentation test
  5fold_splits_v2.pt     cross-validation folds
  data_census.json       per-source counts
verify_data.py           confirms your images are correctly placed
```

---

## Usage

```bash
tar -xzf data.tar.gz
python verify_data.py
```

Expected output:

```
TOTAL                      3535        0
All referenced files present. The splits will reproduce.
```

The script reports per-directory counts and names any missing files, so a
partial or mis-nested extraction is caught before you train on it.

### Expected layout

```
data/
├── FUSeg/
│   ├── train/images/   610      train/labels/   610
│   ├── val/images/     400      val/labels/     400
│   └── test/images/    200      (no public labels)
├── AZH/
│   ├── train/   BG 75 · diabetic 139 · "no wound" 75 ·
│   │            pressure 100 · surgical 122 · venous 185
│   └── test/    BG 25 · diabetic 46 · "no wound" 25 ·
│                pressure 34 · surgical 42 · venous 62
└── Medetec/
    ├── diabetic/   48        pressure/  170
    └── toes/       34        venous/    133
```

> The AZH class folder `no wound` contains a space. Keep it.

All manifest paths are relative to the repository root.

---

## Source datasets

| Dataset | Source | Terms |
|:--|:--|:--|
| **FUSeg** | [fusc.grand-challenge.org](https://fusc.grand-challenge.org) | Challenge data-use agreement required |
| **AZH** | UWM Big Data Lab, AZH Wound and Vascular Center | See source repository |
| **Medetec** | [medetec.co.uk](http://www.medetec.co.uk) | See site terms |

Each carries its own licence. **Obtain them from their original sources and
comply with those terms.** The archive here is provided for reproducibility of
the published splits and does not grant any rights over the underlying images.

---

## Reference results

WILLIE test-set results on this benchmark:

| Task | Metric | Score |
|:--|:--|--:|
| Classification | Accuracy | 91.88% |
| Segmentation | Dice | 91.41% |
| Localization | AP@0.5 | 96.23% |

Headline numbers use the 5-fold ensemble with test-time augmentation. See the
paper for the full protocol.

---

## Known limitations

**Duplicate images across splits.** Verified by byte-level comparison:

- One image identical between AZH train and test
  (`train/surgical/10_0.jpg` / `test/surgical/99_0.jpg`). On the 234-image AZH
  test set, the maximum effect on the reported 91.88% accuracy is **0.43
  percentage points** (worst case 91.45%).
- Nine image pairs identical between FUSeg train and validation. Validation is
  used for model selection only and does not enter the reported test Dice.
- Duplicate copies within splits: FUSeg train 10, validation 6, test 7.
- FUSeg train↔test and validation↔test: no duplicates.

**Domain coverage.** Foot, pressure, venous and surgical wound photographs from
a small number of clinical sources. Imaging conditions, camera hardware and
skin-tone distribution are not controlled or documented, and per-skin-tone
performance has not been measured. Treat cross-population generalisation as
untested.

**Class balance.** `no_wound` and background classes derive from AZH only. The
taxonomy merges source-specific labels; the mapping is in
`index/label_map.json`.

**Research use only.** Not validated for clinical decision-making.

---

## Citation

```bibtex
@inproceedings{willie2026,
  title     = {WILLIE: A Unified Vision-Transformer Framework and Benchmark
               for Wound Classification, Segmentation, and Localization},
  author    = {Maddikunta, Gopi Trinadh and Qian, Peizhu},
  booktitle = {Proceedings of the Machine Learning for Healthcare Conference (MLHC)},
  year      = {2026}
}
```

Please also cite FUSeg, AZH and Medetec per their own requirements.

---

Developed in the Qian Group, University of Houston. Advisor: Dr. Peizhu Qian.
Computation performed on the UH *carya* cluster.