Pith Number

pith:WDFD5QSB

pith:2026:WDFD5QSBM36SI7CNNSJBQSLI5R

not attested not anchored not stored refs resolved

MorphoHELM: A Comprehensive Benchmark for Evaluating Representations for Microscopy-Based Morphology Assays

Alex X. Lu, Emre Hayir, Lorin Crawford

No existing model outperforms classic computer vision analytic strategies across all settings for microscopy morphology representations.

arxiv:2605.15383 v1 · 2026-05-14 · cs.CV

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

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{WDFD5QSBM36SI7CNNSJBQSLI5R}

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

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

No existing model outperforms classic computer vision analytic strategies across all settings, which remain the strongest general use-case representations.

C2weakest assumption

The selected tasks, metrics, and simulated batch effect levels accurately and comprehensively capture the ability of representations to detect true biological signals without introducing bias from the choice of evaluation standards or noise models.

C3one line summary

MorphoHELM is a new benchmark for Cell Painting morphology representations that tests methods across increasing batch effect levels and finds classic computer vision strategies remain the strongest general-purpose performers.

References

40 extracted · 40 resolved · 1 Pith anchors

[1] Cell Painting, a high-content image-based assay for morphological pro ﬁling using multiplexed ﬂuorescent dyes 2016 · doi:10.1038/nprot.2016.105

[2] Srijit Seal, Maria-Anna Trapotsi, Ola Spjuth, Shantanu Singh, Jordi Carreras-Puigvert, Nigel Greene, Andreas Bender, and Anne E. Carpenter. Cell painting: a decade of discovery and innovation in cellu 2024 · doi:10.1038/s41592-024-02528-8

[3] Data-analysis strategies for image-based cell profiling 2017 · doi:10.1038/nmeth.4397

[4] Machine learning and image-ba sed proﬁl- ing in drug discovery 2018 · doi:10.1016/j.coisb.2018.05.004

[5] Image-based profiling for drug discovery: due for a machine-learning upgrade?Nat 2021

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:00:55.623900Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

b0ca3ec24166fd247c4d6c92184968ec7ef35b6206372d91ddaeafde08bd3ca3

Aliases

arxiv: 2605.15383 · arxiv_version: 2605.15383v1 · doi: 10.48550/arxiv.2605.15383 · pith_short_12: WDFD5QSBM36S · pith_short_16: WDFD5QSBM36SI7CN · pith_short_8: WDFD5QSB

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/WDFD5QSBM36SI7CNNSJBQSLI5R \
  | 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: b0ca3ec24166fd247c4d6c92184968ec7ef35b6206372d91ddaeafde08bd3ca3

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "0fcb6a5de94a2c7a969f7fb2da14f1c8621c00a8c84fdefb5fdbb9ebc694500c",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-14T20:13:36Z",
    "title_canon_sha256": "e4aa93a82aacc71599ddae0072df1de920077f157588ac4f67b6f537e05d4de3"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15383",
    "kind": "arxiv",
    "version": 1
  }
}