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pith:2026:XFV5UMRQ4OYFAWOAJTBTZF45PM
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ImmuVis: Hyperconvolutional Foundation Model for Imaging Mass Cytometry

Dawid Uchal, Eike Staub, Ewa Szczurek, Jakub Giezga{\l}a, Kacper Pietrzyk, Karol Zagr\'odka, Krzysztof Gogolewski, Marcin Mo\.zejko, Mateusz Sulimowicz, Michal Orzy{\l}owski, Piotr Kupidura, Robert Pieniuta, Szymon {\L}ukasik, Tomasz Noco\'n, Tomasz Si{\l}kowski

ImmuVis generates convolutional kernels on the fly from marker embeddings so one model works with any combination of molecular markers in tissue images.

arxiv:2602.04585 v2 · 2026-02-04 · cs.CV

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Claims

C1strongest claim

ImmuVis introduces marker-adaptive hyperconvolutions that generate convolutional kernels from learned marker embeddings, enabling a single model to operate on arbitrary measured marker subsets without retraining.

C2weakest assumption

That embeddings learned from the pretraining marker set can generate effective kernels for entirely new marker combinations never seen during training or fine-tuning.

C3one line summary

ImmuVis uses hyperconvolutions generated from marker embeddings to create a foundation model that processes variable marker subsets in IMC images, pretrained on 17M patches and providing uncertainty estimates.

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First computed 2026-05-17T23:39:16.357445Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

b96bda3230e3b05059c04cc33c979d7b2ba996241ccb967284366eeb8206bc2a

Aliases

arxiv: 2602.04585 · arxiv_version: 2602.04585v2 · doi: 10.48550/arxiv.2602.04585 · pith_short_12: XFV5UMRQ4OYF · pith_short_16: XFV5UMRQ4OYFAWOA · pith_short_8: XFV5UMRQ
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/XFV5UMRQ4OYFAWOAJTBTZF45PM \
  | 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())"
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Canonical record JSON
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    "submitted_at": "2026-02-04T14:11:33Z",
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