| --- |
| 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. |
|
|
