model · method

Cell-DINO ViT-L

Cell-DINO ViT-L is the method recorded for protein localization classification. This page preserves the configuration reported by Cell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy.

1 evaluations · 1 metric rows

At a glance

Explanatory profile: limited source coverage · Automated source review, 2026-09-16. This does not change the review status of its results.

Inputs, outputs and configuration
Property	Description and evidence
Recorded dataset	HPA-FoVCell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy · Table 2, HPA-FoV section, Cell-DINO row, PL column
Model type	Not extracted or verified for this record.
Known versions	Not extracted or verified for this record.
Training data	Not extracted or verified for this record.
Context limits	Not extracted or verified for this record.
Access	Not extracted or verified for this record.
Code licence	Not extracted or verified for this record.
Weights licence	Not extracted or verified for this record.

How it works

Recorded evaluation

The imported evaluation describes this procedure: Self-supervised microscopy embedding pre-trained on HPA-FoV; downstream protein-localization classifier. Dataset-specific pretraining; the paper does not claim a general-purpose foundation model that generalizes beyond these benchmarks.

Cell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy · Table 2, HPA-FoV section, Cell-DINO row, PL column

Benchmarks and results

Release 2026-09-16-d74d282221a9 · 1 evaluation · 1 metric row. Different protocols are not a single leaderboard.

Evaluated configurationMetricEvidence origin

Results grouped by the exact reported evaluation
Metric and finding	Coverage and uncertainty	Evidence
Cell-DINO ViT-L: protein localization classification Model: Cell-DINO ViT-L · Benchmark: protein localization classification · Dataset: HPA-FoV Self-supervised microscopy embedding pre-trained on HPA-FoV; downstream protein-localization classifier. Dataset-specific pretraining; the paper does not claim a general-purpose foundation model that generalizes beyond these benchmarks. Author-reported evaluation · Evaluation metadata: needs review
65.5% F1 Unit: percent · Direction: unknown	Uncertainty: not reported in legacy extract Scored: Not reported · Eligible: Not reported	source checkedCell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy · Table 2, HPA-FoV section, Cell-DINO row, PL column Source checking is not independent reproduction.

Strengths and limitations

Strengths and considerations

No source-reviewed explanatory claims are recorded here yet.

Limitations and conditions

This is an evidence-limited profile. The recorded evaluation context does not establish performance on other datasets or configurations.Cell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy · Table 2, HPA-FoV section, Cell-DINO row, PL column

Profile review details

Reviewed the existing release record, its source pointer and linked evaluation context. This is not a fresh full-text architecture review or independent reproduction; numerical review status is unchanged.

Stable record: reported-model-808b23c65fbc89

Sources and history

Release 2026-09-16-d74d282221a9 · Record review: needs review

Cell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy · Original source · version of record

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Technical metadata and extraction receipts

Stable ID: reported-model-808b23c65fbc89

areas: cells-tissues
entity level: method
version: Not reported
reported name: Cell-DINO ViT-L
missing metadata: version: not_reported_in_legacy_extract; checkpoint revision: not_reported_in_legacy_extract; training data: not_reported_in_legacy_extract; licence: not_reported_in_legacy_extract

Related records

model: Cell-DINO ViT-L: protein localization classification

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