Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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D-Synth: Synthetic Dermoscopic Dataset with Pixel-Perfect 3D Information

D-Synth is the first synthetic dermoscopic dataset providing pixel-perfect 3D ground truth (metric depth, surface normals, camera intrinsics) for monocular depth estimation in dermatology. It was introduced in DermDepth: Toward Monocular Metric Scale 3D Reconstruction Models for Dermatology (Carrión & Norouzi, MICCAI 2026).

Project Page / GitHub | Paper

Overview

3,170 rendered dermoscopic samples at 12–20 mm capture distance, 75° field of view
Per-sample assets (inside sample_XXXXXX/):
- image.png — RGB rendering
- depth.png — pixel-perfect metric depth map
- meta.json — camera intrinsics and other metadata
- generation_params.json — full rendering parameters
- (subset) render_rgb.png, render_depth.png, render_meta.json — additional render variants

Sample Usage

You can download the dataset locally using the Hugging Face CLI:

hf download hcarrion/D-Synth --repo-type dataset --local-dir data/dermdepth_train/dsynth

Or programmatically via Python:

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id='hcarrion/D-Synth', 
    repo_type='dataset', 
    local_dir='data/dermdepth_train/dsynth'
)

Generation pipeline

D-Synth extends S-SYNTH (Kim et al., MICCAI 2024) with:

Per-pixel metric depth export
Surface normal map export
Camera intrinsics export
Multiple camera angles
Multiple lesions per scene

It inherits S-SYNTH's anatomically-grounded realism stack:

Probabilistic lesion growth models
Layered melanosome / blood / lipid models across epidermis / dermis / hypodermis
Physics-based light scatter across wavelengths and skin tones

Citation

If you use D-Synth, please cite both DermDepth and the underlying S-SYNTH framework:

@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}
}

@inproceedings{kim2024ssynth,
  title     = {S-SYNTH: Knowledge-Based, Synthetic Generation of Skin Images},
  author    = {Kim, Andrea and others},
  booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
  year      = {2024}
}

License

CC BY-NC 4.0 (research / non-commercial use). For other uses, please contact the authors.

Related Resources

Code & training scripts: https://github.com/hectorcarrion/dermdepth
Fine-tuned DermDepth checkpoints: https://huggingface.co/hcarrion/DermDepth
Base depth model: https://huggingface.co/Ruicheng/moge-2-vitl-normal

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Paper for hcarrion/D-Synth

DermDepth: Toward Monocular Metric Scale 3D Reconstruction Models for Dermatology

Paper • 2607.13010 • Published 3 days ago