Dev
DEXA — 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 |
|---|---|---|
| energy_offset | -1.2 – 1.8 | keV |
| soft_tissue | -3.6 – 5.4 | % |
| beam_overlap | -0.024 – 0.036 |
Dev Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | SwinDXA + gradient | 0.804 | 34.8 | 0.966 | 0.85 | ✓ Certified | Liu et al., ICCV 2021 (DEXA adapt.) |
| 2 | PhysDXA + gradient | 0.768 | 31.38 | 0.936 | 0.9 | ✓ Certified | Raissi et al., J. Comput. Phys. 2019 (DEXA) |
| 3 | DiffusionDXA + gradient | 0.747 | 31.2 | 0.933 | 0.81 | ✓ Certified | Blattmann et al., arXiv 2023 (DEXA adapt.) |
| 4 | PnP-DXA + gradient | 0.730 | 29.11 | 0.902 | 0.89 | ✓ Certified | Venkatakrishnan et al., 2013 (DEXA adapt.) |
| 5 | DXA-CNN + gradient | 0.686 | 27.48 | 0.869 | 0.82 | ✓ Certified | Lee et al., Bone 2020 |
| 6 | BML-Sep + gradient | 0.658 | 26.14 | 0.836 | 0.81 | ✓ Certified | Lehmann et al., Med. Phys. 1981 |
| 7 | DXA-U-Net + gradient | 0.651 | 25.18 | 0.808 | 0.88 | ✓ Certified | Huo et al., IEEE TMED 2021 |
| 8 | TV-DEXA + gradient | 0.604 | 23.36 | 0.745 | 0.86 | ✓ Certified | Sidky & Pan, Phys. Med. Biol. 2008 (DEXA) |
| 9 | FBP-DEXA + gradient | 0.600 | 23.77 | 0.76 | 0.79 | ✓ Certified | Mazess et al., Am. J. Clin. Nutr. 1990 |
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%