Public
MALDI Mass Spectrometry Imaging — Public Tier
(5 scenes)Full-access development tier with all data visible.
What you get
Measurements (y), ideal forward operator (H), spec ranges, ground truth (x_true), and true mismatch spec.
How to use
Load HDF5 → compare reconstruction vs x_true → check consistency → iterate.
What to submit
Reconstructed signals (x_hat) and corrected spec as HDF5.
Parameter Specifications
✓
True spec visible — use these exact values for Scenario III oracle reconstruction.
| Parameter | Spec Range | True Value | Unit |
|---|---|---|---|
| laser_fluence_drift | 0.96 – 1.08 | 1.02 | - |
| mass_accuracy | -1.0 – 2.0 | 0.5 | ppm |
| extraction_delay | 96.0 – 108.0 | 102.0 | ns |
| matrix_crystallization | 0.94 – 1.12 | 1.03 | - |
InverseNet Baseline Scores
Method: CPU_baseline — Mismatch parameter: nominal
Scenario I (Ideal)
33.04 dB
SSIM 0.8313
Scenario II (Mismatch)
21.66 dB
SSIM 0.3376
Scenario III (Oracle)
22.75 dB
SSIM 0.4197
Per-scene breakdown (4 scenes)
| Scene | PSNR I | SSIM I | PSNR II | SSIM II | PSNR III | SSIM III |
|---|---|---|---|---|---|---|
| scene_00 | 32.90 | 0.8330 | 21.36 | 0.3538 | 22.69 | 0.4393 |
| scene_01 | 32.93 | 0.8361 | 21.23 | 0.3703 | 22.66 | 0.4596 |
| scene_02 | 33.03 | 0.8183 | 21.98 | 0.3053 | 22.85 | 0.3832 |
| scene_03 | 33.32 | 0.8377 | 22.06 | 0.3211 | 22.79 | 0.3966 |
Public Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | CalibFormer + gradient | 0.775 | 31.14 | 0.933 | 0.95 | ✓ Certified | Transformer calibration, 2024 |
| 2 | ResNet-Calib + gradient | 0.727 | 28.91 | 0.899 | 0.89 | ✓ Certified | ResNet for calibration, 2022 |
| 3 | Instrument-CNN + gradient | 0.722 | 28.1 | 0.883 | 0.94 | ✓ Certified | Instrument-specific CNN |
| 4 | MassSpecFormer + gradient | 0.714 | 28.32 | 0.887 | 0.88 | ✓ Certified | Mass spectrometry transformer, 2024 |
| 5 | DiffusionInstrumentation + gradient | 0.712 | 28.12 | 0.883 | 0.89 | ✓ Certified | Zhang et al., 2024 |
| 6 | PnP-BM3D + gradient | 0.685 | 26.41 | 0.843 | 0.92 | ✓ Certified | Danielyan et al., 2012 |
| 7 | ScoreInstrumentation + gradient | 0.682 | 26.55 | 0.847 | 0.89 | ✓ Certified | Wei et al., 2025 |
| 8 | PnP-NLM + gradient | 0.660 | 24.96 | 0.801 | 0.95 | ✓ Certified | Non-local means filter |
| 9 | Peak Fitting + gradient | 0.643 | 24.24 | 0.777 | 0.95 | ✓ Certified | Gaussian peak fitting |
| 10 | Calibration-Lookup + gradient | 0.597 | 22.42 | 0.708 | 0.95 | ✓ Certified | Look-up table calibration |
| 11 | Deconv + gradient | 0.557 | 21.25 | 0.657 | 0.91 | ✓ Certified | Analytical baseline |
Visible Data Fields
y
H_ideal
spec_ranges
x_true
true_spec
Dataset
Format: HDF5
Scenes: 5
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%