--- 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- 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
WILLIE benchmark overview Three public wound datasets unified into a single 5-class taxonomy with fixed splits for classification, segmentation and localization.
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//) - **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.