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pith:LVBP5KYS

pith:2026:LVBP5KYSOPUUTKKHBBS56BZW57
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MicroscopyMatching: Towards a Ready-to-use Framework for Microscopy Image Analysis in Diverse Conditions

Haoxuan Qu, Hossein Rahmani, Jeff W. Lichtman, Jun Liu, Shuohong Wang, Xiaofei Hui

MicroscopyMatching reformulates diverse microscopy analysis tasks as matching problems solved by pre-trained latent diffusion models to enable reliable segmentation, tracking, and counting across varied conditions without adaptation.

arxiv:2605.14980 v1 · 2026-05-14 · cs.CV · cs.AI

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Claims

C1strongest claim

we present the first ready-to-use microscopy image analysis framework, MicroscopyMatching, that can reliably perform key analysis tasks (including segmentation, tracking, and counting) across diverse microscopy analysis settings. ... MicroscopyMatching reformulates diverse microscopy image analysis tasks as a unified matching problem, effectively handling this problem by exploiting the robust matching capability from pre-trained latent diffusion models.

C2weakest assumption

That pre-trained latent diffusion models possess robust matching capability sufficient to generalize across the substantial diversity of biological object types, sample processing protocols, imaging equipment, and analysis tasks without any adaptation or fine-tuning.

C3one line summary

MicroscopyMatching reformulates diverse microscopy analysis tasks as matching problems solved by pre-trained latent diffusion models to enable reliable segmentation, tracking, and counting across varied conditions without adaptation.

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Receipt and verification
First computed 2026-05-17T23:38:55.116813Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

5d42feab1273e949a9470865df0736efd484cef0425da7bf3789ff94ce622974

Aliases

arxiv: 2605.14980 · arxiv_version: 2605.14980v1 · doi: 10.48550/arxiv.2605.14980 · pith_short_12: LVBP5KYSOPUU · pith_short_16: LVBP5KYSOPUUTKKH · pith_short_8: LVBP5KYS
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/LVBP5KYSOPUUTKKHBBS56BZW57 \
  | 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: 5d42feab1273e949a9470865df0736efd484cef0425da7bf3789ff94ce622974
Canonical record JSON
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      "cs.AI"
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-14T15:44:26Z",
    "title_canon_sha256": "c9d0430bd31b984bb60783c7f677c8f9a6a29a71a779fca0cb5c236b85f3447b"
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