| --- |
| license: cc-by-nc-4.0 |
| tags: |
| - depth-estimation |
| - dermatology |
| - 3d-reconstruction |
| - medical-imaging |
| - skin-lesion |
| - moge |
| base_model: Ruicheng/moge-2-vitl-normal |
| library_name: pytorch |
| pipeline_tag: depth-estimation |
| --- |
| |
| # DermDepth: Monocular Metric Scale 3D Reconstruction for Dermatology |
|
|
| Model checkpoints from DermDepth (Carrión & Norouzi, MICCAI 2026). |
|
|
| ## Checkpoints |
|
|
| | Filename | Training data | Notes | |
| |---|---|---| |
| | `DermDepth_Synth.pt` | D-Synth only | "DermDepth_S" in the paper. Synthetic-only baseline. | |
| | `DermDepth_Synth_SKINL2_WoundsDB.pt` | D-Synth → SKINL2 + WoundsDB | Intermediate stage (before DDI pseudo-GT). | |
| | `DermDepth_Synth_SKINL2_WoundsDB_DDI.pt` | D-Synth → SKINL2 + WoundsDB → DDI pseudo-GT | **Best model. Corresponds to "DermDepth" in the paper.** | |
| | `DermDepth_Synth_Normals.pt` | D-Synth, normal-head emphasis | Trained normal-head model. | |
|
|
| ## Key results (held-out test sets, paper Table 1) |
|
|
| | Method | SKINL2 Scale | WoundsDB Scale | DDI Ratio | Fitzpatrick Disparity | |
| |---|:---:|:---:|:---:|:---:| |
| | MoGe-2 (baseline) | 16.10× | 0.62× | 81.0× | 10.90 | |
| | DermDepth_Synth | 1.11× | 0.28× | 9.2× | 1.70 | |
| | **DermDepth (best)** | **0.87×** | **0.91×** | **1.95×** | **1.02** | |
| |
| Scale ratio target is 1.0×. See the paper for full benchmarks and SI-δ₁ details. |
| |
| ## Usage |
| |
| These checkpoints are designed to be loaded by [MoGe-2](https://github.com/microsoft/MoGe), modified per the [DermDepth code repository](https://github.com/hectorcarrion/dermdepth). The repo contains end-to-end training, inference, and evaluation scripts. |
| |
| Quick download: |
| |
| ```python |
| from huggingface_hub import hf_hub_download |
| ckpt = hf_hub_download( |
| repo_id="hcarrion/DermDepth", |
| filename="DermDepth_Synth_SKINL2_WoundsDB_DDI.pt", |
| ) |
| ``` |
| |
| ## Citation |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
| ## License |
|
|
| CC BY-NC 4.0 (research / non-commercial use). The base MoGe-2 weights remain under their original license — see [the MoGe repository](https://github.com/microsoft/MoGe) for details. |
|
|
| ## Related Resources |
|
|
| - **Code & training scripts**: https://github.com/hectorcarrion/dermdepth |
| - **D-Synth dataset (training data)**: https://huggingface.co/datasets/hcarrion/D-Synth |
| - **Base model**: https://huggingface.co/Ruicheng/moge-2-vitl-normal |
|
|
