Dev
DOT — Dev Tier
(3 scenes)Blind evaluation tier — no ground truth available.
What you get
Measurements (y), ideal forward operator (H), and spec ranges only.
How to use
Apply your pipeline from the Public tier. Use consistency as self-check.
What to submit
Reconstructed signals and corrected spec. Scored server-side.
Parameter Specifications
🔒
True spec hidden — estimate parameters from spec ranges below.
| Parameter | Spec Range | Unit |
|---|---|---|
| mu_a | -12.0 – 18.0 | % |
| mu_s | -9.6 – 14.4 | % |
| source_pos | -1.2 – 1.8 | mm |
Dev Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | DiffusionDOT + gradient | 0.788 | 33.47 | 0.957 | 0.85 | ✓ Certified | Gao et al., NeurIPS 2024 |
| 2 | PhysDOT + gradient | 0.739 | 29.65 | 0.911 | 0.89 | ✓ Certified | Chen et al., Opt. Express 2024 |
| 3 | TransDOT + gradient | 0.733 | 29.49 | 0.909 | 0.87 | ✓ Certified | Li et al., IEEE TMI 2022 |
| 4 | SwinDOT + gradient | 0.727 | 29.87 | 0.915 | 0.81 | ✓ Certified | Wang et al., Biomed. Opt. Express 2023 |
| 5 | FEM-DOT + gradient | 0.595 | 23.42 | 0.747 | 0.81 | ✓ Certified | Schweiger et al., J. Biomed. Opt. 2005 |
| 6 | DOT-Net + gradient | 0.517 | 20.68 | 0.631 | 0.79 | ✓ Certified | Guo et al., Biomed. Opt. Express 2021 |
| 7 | DnCNN-DOT + gradient | 0.498 | 19.76 | 0.587 | 0.83 | ✓ Certified | Yoo et al., Sci. Rep. 2019 |
| 8 | Born-Approx + gradient | 0.423 | 17.35 | 0.468 | 0.81 | ✓ Certified | Arridge, Inverse Probl. 1999 |
| 9 | TV-DOT + gradient | 0.358 | 14.99 | 0.354 | 0.83 | ✓ Certified | Borsic et al., IEEE TMI 2010 |
Visible Data Fields
y
H_ideal
spec_ranges
Dataset
Format: HDF5
Scenes: 3
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%