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pith:IBO3S53M

pith:2026:IBO3S53MLIB2AOUSIKXE5OHYCS
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DMDSC: A Dynamic-Margin Deep Simplex Classifier for Open-Set Recognition on Medical Image Datasets

Arnav Aditya, Nitin Kumar, Saurabh J. Shigwan, Vishal

Dynamic margins based on label frequency improve open-set recognition for rare medical conditions.

arxiv:2605.00675 v2 · 2026-05-01 · cs.CV

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Claims

C1strongest claim

Our approach automatically adapts class-specific margins based on label frequency, enforcing a higher penalty and tighter feature clustering for rare pathologies to counteract the effects of data imbalance. Extensive experiments ... demonstrate that our framework outperforms state-of-the-art methods.

C2weakest assumption

That a margin scaled solely by label frequency will produce tighter, more separable clusters for rare classes without introducing new overfitting to frequency statistics or harming performance on common classes.

C3one line summary

DMDSC adapts simplex-classifier margins dynamically according to label frequency to tighten clustering on rare medical classes and improve open-set rejection on imbalanced imaging datasets.

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First computed 2026-06-30T01:17:39.000249Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

405db9776c5a03a03a9242ae4eb8f81481259937ad85400838707f0398c6fa17

Aliases

arxiv: 2605.00675 · arxiv_version: 2605.00675v2 · doi: 10.48550/arxiv.2605.00675 · pith_short_12: IBO3S53MLIB2 · pith_short_16: IBO3S53MLIB2AOUS · pith_short_8: IBO3S53M
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/IBO3S53MLIB2AOUSIKXE5OHYCS \
  | 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: 405db9776c5a03a03a9242ae4eb8f81481259937ad85400838707f0398c6fa17
Canonical record JSON
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    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-01T13:57:41Z",
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