Pith Number

pith:CAPME2KJ

pith:2026:CAPME2KJDFSMKE7VRWNIHUC6LX

not attested not anchored not stored refs pending

Towards Modality-Agnostic Medical Image Anomaly Detection: A Training-Free Manifold Refinement Approach

Gouri Lakshmi S, Pritam Kar, Saptarshi Bej

Mean Shift Density Enhancement refines latent features from pretrained backbones to sharpen one-class anomaly scoring in medical images.

arxiv:2604.19191 v2 · 2026-04-21 · cs.CV · cs.AI

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\usepackage{pith}
\pithnumber{CAPME2KJDFSMKE7VRWNIHUC6LX}

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

MSDE achieves the highest AUC on four datasets and the highest Average Precision on five datasets, including near-perfect performance on brain tumor detection (0.981 AUC/AP).

C2weakest assumption

That the iterative manifold-shifting performed by Mean Shift Density Enhancement reliably moves normal samples toward higher-likelihood regions in the latent space of arbitrary pretrained backbones, thereby sharpening the subsequent Gaussian density estimate for anomaly scoring.

C3one line summary

A new Mean Shift Density Enhancement procedure applied to self-supervised embeddings yields state-of-the-art anomaly detection AUC and average precision on seven medical imaging datasets.

Receipt and verification

First computed	2026-06-30T01:16:30.054884Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

101ec269491964c513f58d9a83d05e5ddbae08b03079464f5a86567981c4b606

Aliases

arxiv: 2604.19191 · arxiv_version: 2604.19191v2 · doi: 10.48550/arxiv.2604.19191 · pith_short_12: CAPME2KJDFSM · pith_short_16: CAPME2KJDFSMKE7V · pith_short_8: CAPME2KJ

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/CAPME2KJDFSMKE7VRWNIHUC6LX \
  | 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: 101ec269491964c513f58d9a83d05e5ddbae08b03079464f5a86567981c4b606

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "9d0eae0cb8656a187a83f681977a39ffdfff081f23d0f37b632aa768aa5381da",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-04-21T07:58:44Z",
    "title_canon_sha256": "2f350d25ff3cb5168fd6facd99724f111f661f8c30f422cd5f2cdacf7039baf8"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.19191",
    "kind": "arxiv",
    "version": 2
  }
}