NSP-StageA-FusionCNN-25v

Potestates/NSP-StageA-FusionCNN-25v

NSP-StageA-FusionCNN-25v is a machine learning model from Potestates. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as other.

Stage-A FusionCNN trained with the released NSPR2GS training entry (deployment/nspgsabcv1, branch migration/gmrb-r2fine-20260907, commit 4714de6), on four datasets instead of LUNA16 only.

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Updated Sep 19, 2026

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License: other
Access: Public
Created: Sep 19, 2026
Updated: Sep 19, 2026
SHA: 49849053

Model card

From the Hugging Face model README

NSP-GS Stage-A FusionCNN — 25-view DeepSparse prior, 4 datasets

Stage-A FusionCNN trained with the released NSP_R2GS training entry (deployment/nsp_gs_abc_v1, branch migration/gmrb-r2fine-20260907, commit 4714de6), on four datasets instead of LUNA16 only.

Inputs per case

Input	Source
V1	DeepSparse 25-view output (`logs/<dst>+25v+n250+s2/ep_400.pth`, see `Potestates/DeepSparse-25v`), GT-free inference
V2	prior-assisted R²-GS (V6 `train.py`, RUN_COMMANDS 3.2 NSP-only: `lambda_null 0.05`, null loss 10k→20k), 20k iterations, prior = V1, same 25 views
features / labels	released `pair` → `features` → `labels` chain (75D F+M+D patch features, gate = `actionable`, beta = `beta_unclipped`)

25 views = uniform over 180° (indices 0,12,…,288 of a 300-view cone-beam bank). All four datasets use their official DeepSparse projector geometry.

Cohort

One model for all four datasets. Cases were never used to train the DeepSparse 25v models.

Dataset	train	validation
LUNA16 (lung)	40	5
PENGWIN (pelvis)	35	5
PANORAMA (abdomen)	40	5
ToothFairy (tooth)	40	5
total	155	20

Lists: cohort/STAGEA_SPLIT.json, frozen manifest cohort/TRAINING_DATASET.json.

Training

Released formal defaults (AdamW lr 1e-3, wd 1e-4, batch 64, 30 epochs, patience 8, 2048 patches/patient/epoch, AMP, seed 20260911, patient-balanced), with one override: --num-workers 16 (loader throughput only; the fixed sampler makes the data order identical). The run is therefore labelled EXPLICIT_NONFORMAL_OVERRIDE.

best validation patch loss 0.06130 (epoch 29 of 30), 956 s on one RTX PRO 6000 Blackwell, peak CUDA memory 0.46 GB
use fusion_cnn_run_001/best.pt together with fusion_cnn_run_001/PREPROCESSING.npz (fit on the train split only)

Results — validation (20 cases, in-sample: used for checkpoint selection)

3D PSNR (dB) vs GT, skimage, data_range 1:

Dataset	V1 DeepSparse 25v	V2 R²-GS 20k	V_A	V_A − V1
LUNA16	31.64	30.47	33.15	+1.51
PENGWIN	30.45	29.84	31.16	+0.71
PANORAMA	29.48	28.22	29.61	+0.12
ToothFairy	34.52	33.89	36.75	+2.22
all	31.52	30.60	32.67	+1.14

Per-case: fusion_cnn_run_001/scores_validation.csv. 18/20 cases improve; PENGWIN 032 (−3.00) and PANORAMA 101749 (−0.85) do not.

Results — held-out test (15 cases, never used for training or checkpoint selection)

5 LUNA16 + 5 PANORAMA + 5 ToothFairy (every non-training PENGWIN case is already in train/validation). V2 and features were built GT-free; GT was opened only for scoring.

Dataset	V1 DeepSparse 25v	V2 R²-GS 20k	V_A	V_A − V1
LUNA16	30.06	28.45	31.44	+1.38
PANORAMA	29.71	29.30	30.80	+1.08
ToothFairy	33.78	33.47	35.86	+2.08
all	31.19	30.41	32.70	+1.52

All 15/15 cases improve over V1 (+0.53 to +2.37 dB). Per-case: fusion_cnn_run_001/scores_test_heldout.csv.

Local changes to the released code

patches/build_training_labels_nonneg_fix.diff: clamps the patch sums of squares (d2, target2, p1_sq, p2_sq) at 0. The summed-area-table subtraction returned −5.3e−23 for one all-air patch (PENGWIN 004), which made cosine NaN and aborted the case. No other label value changes (175/175 label files finite; only that one patch was clamped).
patches/r2gs_cuda_headers_blackwell.diff: #include <cfloat> / <cstdint> so the R²-GS CUDA extensions build with CUDA 12.9 (torch 2.7.1+cu128).

License

Research use. Weights derive from LUNA16, PENGWIN (CC BY 4.0), PANORAMA (CC BY-NC 4.0, non-commercial) and ToothFairy; check each dataset's terms.

License: other
Created: Sep 19, 2026
Updated: Sep 19, 2026