Pith Number

pith:5T2PE3XO

pith:2026:5T2PE3XOKTLU3NICDH6W2DSJLL

not attested not anchored not stored refs resolved

FedStain: Modeling Higher-Order Stain Statistics for Federated Domain Generalization in Computational Pathology

Fengyi Zhang, Junya Zhang, Wenzhuo Sun

FedStain lets sites share skewness and kurtosis of stain colors to train pathology models that generalize across institutions.

arxiv:2605.14590 v1 · 2026-05-14 · cs.CV

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{5T2PE3XOKTLU3NICDH6W2DSJLL}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

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

To our knowledge, FedStain is the first FedDG approach to explicitly model higher-order stain statistics, enabling robust cross-institutional deployment in computational pathology. FedStain yields consistent improvements, outperforming state-of-the-art FedL, DG, and FedDG baselines by up to +3.9% absolute accuracy.

C2weakest assumption

That skewness and kurtosis, when exchanged as compact descriptors, sufficiently capture the dominant non-Gaussian stain variability and that the contrastive cross-site aggregation produces stain-invariant representations without relaxing privacy constraints.

C3one line summary

FedStain improves federated domain generalization in computational pathology by exchanging higher-order stain moments (skewness, kurtosis) to capture non-Gaussian variability, outperforming baselines by up to 3.9% accuracy.

References

76 extracted · 76 resolved · 0 Pith anchors

[1] A Survey on Deep Learning in Medical Image Analysis, 2017

[2] Machine Learning Methods for Histopathological Image Analysis, 2018

[3] Summary of the HIPAA Privacy Rule, 1996

[4] Regulation (EU) 2016/679: General Data Protection Regulation, 2016

[5] Quantifying the Effects of Data Augmentation and Stain Color Normalization in Convolutional Neural Networks for Computational Pathology, 2019

Formal links

1 machine-checked theorem link

Receipt and verification

First computed	2026-05-17T23:39:05.255251Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

ecf4f26eee54d74db50219fd6d0e495af032e901604ccbda48fa437c4aab653d

Aliases

arxiv: 2605.14590 · arxiv_version: 2605.14590v1 · doi: 10.48550/arxiv.2605.14590 · pith_short_12: 5T2PE3XOKTLU · pith_short_16: 5T2PE3XOKTLU3NIC · pith_short_8: 5T2PE3XO

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/5T2PE3XOKTLU3NICDH6W2DSJLL \
  | 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: ecf4f26eee54d74db50219fd6d0e495af032e901604ccbda48fa437c4aab653d

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "e06d174f850632fa44fd30d4dfbe5848610a825aebf1aac0b34e538f9286b740",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-14T09:00:04Z",
    "title_canon_sha256": "6951f88e5aefd1fc5f9d4ea1c0c9d921d4d17a74978465a64839a6d5f0dd86b2"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14590",
    "kind": "arxiv",
    "version": 1
  }
}