---
license: mit
library_name: pytorch
pipeline_tag: image-segmentation
base_model: facebook/dinov2-base
tags:
  - medical-imaging
  - diabetic-foot-ulcer
  - wound-segmentation
  - image-classification
  - image-segmentation
  - dinov2
  - clinical-ai
---

# DiaFoot.AI — Diabetic Foot Ulcer Triage & Wound Segmentation

Cascaded multi-task model for diabetic foot images: it **triages** a foot photo into
`Healthy` / `Non-DFU` / `DFU`, **segments** the wound when present, and **measures** wound
area in mm². Both stages use a frozen **DINOv2 ViT-B/14** backbone with a trainable head
(classifier) / UPerNet decoder (segmenter).

Code: <https://github.com/Ruthvik-Bandari/DiaFoot.AI>

> **Honesty note.** Every number below is from leakage-audited clean-split evaluation
> (`results/*.json` in the repo). They are lower than an earlier headline (Dice 85.89%) that
> was a DFU-only subgroup measured before a data-leakage fix. These are the honest numbers.

## Files

| File | Stage | Backbone |
|---|---|---|
| `dinov2_classifier.pt` | 3-class triage (Healthy/Non-DFU/DFU) | DINOv2 ViT-B/14 + linear head |
| `dinov2_segmenter.pt` | Binary wound segmentation | DINOv2 ViT-B/14 + UPerNet decoder |

## Results (leakage-audited test set, n = 1,161)

### Triage classification
| Metric | Value |
|---|---|
| Accuracy | 0.984 |
| Macro F1 | 0.981 |
| Macro AUROC | 0.999 |
| DFU sensitivity | 0.966 |
| Healthy specificity | 0.995 |
| ECE (after temperature scaling) | 0.007 |
| Defer @ 0.95 confidence | 93.5% coverage, 99.7% accuracy on kept |

### Wound segmentation
| Slice | Dice | IoU | HD95 (px) |
|---|---|---|---|
| DFU wounds only (n = 263) | 0.891 | 0.829 | 11.3 |
| Full mixed test set, mean | 0.718 | 0.673 | 66.1 |
| Full mixed test set, median | 0.929 | 0.868 | 5.0 |
| 5-fold CV (DFU) | 0.853 ± 0.009 | 0.785 ± 0.010 | — |

The mixed-set mean is far below the median because healthy/non-DFU images have empty masks
(any false-positive pixel scores Dice ≈ 0). Judge wound quality from the DFU-only / median rows.

## Intended use

Research and education on diabetic-foot-ulcer imaging. **Not a medical device**; no regulatory
clearance. Do not use for diagnosis or treatment.

## Limitations (read before use)

- **The triage classifier does not generalize across image sources.** On unseen datasets,
  external accuracy drops to ~21% and DFU sensitivity to 0%. Re-validate on any new source
  before use. The segmenter transfers well (external DFU Dice 0.893).
- **Fairness is under-powered by the test split** (effectively one ITA skin-tone group); the
  DFU-only fairness gap is 0.00, but broad skin-tone fairness is unproven.
- Clinical wound-area agreement was measured on only n = 3 (indicative, not validated).

## How to use

```python
import torch
from huggingface_hub import hf_hub_download

repo = "Ruthvik-Bandari/DiaFoot.AI"
clf = hf_hub_download(repo, "dinov2_classifier.pt")
seg = hf_hub_download(repo, "dinov2_segmenter.pt")
# Load with the model definitions in src/models/ from the GitHub repo:
#   DINOv2Classifier(backbone="dinov2_vitb14"), DINOv2Segmenter(backbone="dinov2_vitb14")
# Or run the end-to-end CLI:
#   python scripts/predict.py --image foot.jpg \
#     --classifier-checkpoint dinov2_classifier.pt \
#     --segmenter-checkpoint dinov2_segmenter.pt --device cpu
```

Input size 518×518 (DINOv2). See the GitHub repo for the full inference pipeline, FastAPI
service, and ONNX export.

## Training data

~8,105 images across three categories (2,119 DFU incl. AZH · 3,300 healthy · 2,686 non-DFU),
aggregated from FUSeg, AZH, Kaggle, and Mendeley sources. Splits are 70/15/15, doubly
stratified by ITA skin tone and class, and audited to zero train/test leakage
(`has_any_leakage: False`). The raw images are **not** redistributed here (third-party
licenses + patient privacy).

## Citation

Bandari, R. *DiaFoot.AI v2: Diabetic Foot Ulcer Detection, Segmentation & Wagner Staging.* 2026.
<https://github.com/Ruthvik-Bandari/DiaFoot.AI>

Built on DINOv2 (Meta AI, Apache-2.0). Fine-tuned weights released under MIT.