pith:4QGQRAUK
InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction
Dual-branch CNN-Transformer predicts three-zone glioma infiltration risk from multimodal MRI.
arxiv:2605.02230 v2 · 2026-05-04 · cs.CV · cs.LG
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Claims
InfiltrNet outperforms five established baselines on BraTS 2020 and BraTS 2025 for predicting three-zone infiltration risk maps from multimodal MRI.
The distance-transform strategy on BraTS annotations produces labels that accurately reflect true biological infiltration risk rather than just geometric distance from the visible tumor.
InfiltrNet uses a CNN-Transformer hybrid with distance-transform labels to generate infiltration risk maps from MRI and outperforms baselines on BraTS 2020 and 2025 datasets.
Formal links
Receipt and verification
| First computed | 2026-06-23T02:12:49.520565Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
e40d08828af4dfea8ffdb60a1f8344a902744efc4cd673f53ccf248b77e2491d
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/4QGQRAUK6TP6VD75WYFB7A2EVE \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: e40d08828af4dfea8ffdb60a1f8344a902744efc4cd673f53ccf248b77e2491d
Canonical record JSON
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