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pith:5LFIMW6W

pith:2025:5LFIMW6W457YNZF6XDUY7HNGII
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Beyond Nearest Neighbor Interpolation in Data Augmentation

Olivier Rukundo

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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3 Author claim open · sign in to claim
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Claims

C1strongest claim

Experimental evaluation on three medical image segmentation datasets and the XBAT+ datasets demonstrated performance gains across multiple quantitative metrics.

C2weakest assumption

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.

C3one line summary

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

arxiv: 2504.01527 · arxiv_version: 2504.01527v4 · doi: 10.48550/arxiv.2504.01527 · pith_short_12: 5LFIMW6W457Y · pith_short_16: 5LFIMW6W457YNZF6 · pith_short_8: 5LFIMW6W
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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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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2025-04-02T09:13:18Z",
    "title_canon_sha256": "a6de217bf5863770fd6124b34a76dc1763f46d2544f7c2982382c6c89815fc1e"
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