pith:5LFIMW6W
Beyond Nearest Neighbor Interpolation in Data Augmentation
Replacing nearest neighbor interpolation with alternative methods and a mean-based class filter improves performance in medical image segmentation.
arxiv:2504.01527 v4 · 2025-04-02 · cs.CV · eess.IV
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\pithnumber{5LFIMW6W457YNZF6XDUY7HNGII}
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Record completeness
Claims
Experimental evaluation on three medical image segmentation datasets and the XBAT+ datasets demonstrated performance gains across multiple quantitative metrics.
That the mean-based class filtering mechanism effectively handles undefined categorical labels without introducing new biases or errors in the augmented data while preserving high-frequency details.
A modified geometric transformation and mean-based class filtering in data augmentation pipelines, paired with an offline interpolation-specific pipeline, yields performance gains on medical image segmentation datasets by avoiding nearest-neighbor label and detail issues.
Receipt and verification
| First computed | 2026-06-19T16:11:10.472855Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
eaca865bd6e77f86e4beb8e98f9da642364b016a1827eb71baa01631932a6152
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/5LFIMW6W457YNZF6XDUY7HNGII \
| 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: eaca865bd6e77f86e4beb8e98f9da642364b016a1827eb71baa01631932a6152
Canonical record JSON
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