README.md

metadata

title: DermDepth
emoji: 🩺
colorFrom: indigo
colorTo: pink
sdk: gradio
sdk_version: 6.20.0
app_file: app.py
short_description: Metric-scale 3D from a single dermatology photo
python_version: '3.12'
startup_duration_timeout: 30m
license: cc-by-nc-4.0
tags:
  - depth-estimation
  - 3d-reconstruction
  - dermatology
  - medical-imaging
  - skin-lesion
  - moge
models:
  - hcarrion/DermDepth
  - Ruicheng/moge-2-vitl-normal
datasets:
  - hcarrion/D-Synth

🩺 DermDepth — Monocular Metric-Scale 3D for Dermatology

Official demo for DermDepth: Toward Monocular Metric Scale 3D Reconstruction Models for Dermatology (Carrión & Norouzi, MICCAI 2026).

Dermatology is largely a measurement problem — clinicians screen and monitor lesions and wounds by tracking size, border, elevation and texture over time. Those properties are inherently 3D, yet point-of-care imaging is almost always a single 2D photograph.

DermDepth recovers metric-scale 3D from one ordinary photo — no depth sensor, no second view, no ruler in frame. A 2.1M-parameter scale-and-normal head sits on a frozen MoGe-2 backbone and is trained progressively on D-Synth (synthetic renders with pixel-perfect depth, normals and intrinsics) and then on real clinical data.

Benchmark results from the paper (held-out test sets) — accuracy on your own photograph may be worse:

	MoGe-2 baseline	DermDepth
SKINL2 scale ratio (target 1.0×)	16.10×	0.87×
WoundsDB scale ratio	0.62×	0.91×
DDI scale ratio	81.0×	1.95×
Fitzpatrick scale disparity	10.90	1.02

Two checkpoints, on purpose

Output	Checkpoint	Why
Metric depth + 3D mesh	DermDepth_Synth_SKINL2_WoundsDB_DDI.pt	The paper's best model (D-Synth → SKINL2 + WoundsDB → DDI pseudo-GT); best metric scale and lowest skin-tone disparity.
Surface normals	DermDepth_Synth_Normals.pt	The dedicated normal-head model. Real clinical normal ground truth is noisy (SKINL2 plenoptic depth has local planar noise; WoundsDB ToF is sparse and offset from RGB), so D-Synth's rendered normals are the only clean normal supervision.

These are two complete, separately-trained models that do not share a trunk, so the demo loads both rather than swapping a head.

Inference resolution

MoGe-2 converts resolution_level into a ViT token budget over num_tokens_range = [1200, 3600]. This demo addresses num_tokens directly and keeps every option inside that range — "Ultra" (3600) is the model's genuine maximum, and is the default.

Examples & credits

The first three examples are real clinical photographs from WoundsDB (Chronic Wounds Multimodal Image Database, Silesian University of Technology), used under CC BY 4.0 at their native 320×240 — the resolution the paper evaluates WoundsDB at. They are held-out cases (the paper splits WoundsDB by case: 1–30 train, 31+ test), chosen for anatomical variety: case_45 (leg ulcer), case_33 (hand), case_42 (foot).

Kręcichwost, M., Czajkowska, J., Wijata, A., Juszczyk, J., Pyciński, B., Biesok, M., Rudzki, M., Majewski, J., Kostecki, J., & Pietka, E. (2021). Chronic wounds multimodal image database. Computerized Medical Imaging and Graphics, 88, 101844. doi:10.1016/j.compmedimag.2020.101844

The last three are synthetic renders from D-Synth, released under CC BY-NC 4.0 — one per Fitzpatrick group (I–II, III–IV, V–VI). They are renders, not patient photographs, and imply no clinical diagnosis.

No DDI imagery is bundled: Stanford's Research Use Agreement prohibits redistributing any portion of that dataset.

⚠️ Research demonstration only — not a medical device. Outputs are not diagnostic and must not inform clinical decisions. Predictions on out-of-distribution images can fail silently.

Links

📄 Paper · 🤗 Model · 📊 D-Synth · 💻 Code

@inproceedings{carrion2026dermdepth,
  title     = {DermDepth: Toward Monocular Metric Scale 3D Reconstruction Models for Dermatology},
  author    = {Carri{\'o}n, H{\'e}ctor and Norouzi, Narges},
  booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
  year      = {2026},
  eprint    = {2607.13010},
  archivePrefix = {arXiv}
}

License

Demo code: CC BY-NC 4.0, matching the DermDepth weights. The MoGe-2 base weights remain under their original license. Example images are under their respective licenses, credited above.