Mettle

Mettle is a histopathology tile encoder derived from H-optimus-0. It is designed to improve representation consistency across slide scanners and staining variation while retaining biologically useful morphology.

Mettle is intended for research feature extraction. It is not a diagnostic system and has not been validated for autonomous clinical use.

Model details

Item	Value
Parent model	H-optimus-0
Parent-model license	Apache-2.0
Architecture	ViT-g/14, DINOv2 reg4
Input	224 × 224 RGB histology tile
Compatibility output	1,536-dimensional headed CLS embedding
Public benchmark output	3,072-dimensional CLS+mean-patch embedding
model.safetensors SHA-256	7754ef463e79dd13b072247b56db39791c884db10774e354d76f76367093ce4a

The public benchmark representation concatenates the headed CLS embedding with the mean of the spatial patch tokens:

[headed CLS embedding (1536), mean spatial-patch embedding (1536)]

CLS and register tokens are excluded from the patch mean. The first 1,536 coordinates are exactly the compatibility CLS representation.

Public benchmark results

All results in this table use the 3,072-dimensional CLS+mean representation. Higher is better. Values after ± are standard deviations over five downstream probe runs. PathoROB Robustness Index is a point estimate without an uncertainty interval.

Benchmark	Metric	Mettle	H-optimus-0
PathoROB	Mean Robustness Index	0.92227	0.8015
HEST	Mean Pearson, 9 tasks	0.43032	0.42526
EVA BACH	Accuracy	0.78168 ± 0.00567	0.7294
EVA CRC	Accuracy	0.96912	0.9578
EVA PatchCamelyon	Balanced accuracy	0.9476	0.9441

PathoROB breakdown

Cohort	Mettle
Camelyon	0.93859
TCGA	0.86521
Tolkach ESCA	0.96302
Mean	0.92227

PathoROB does not provide uncertainty estimates for its Robustness Index. Differences in point estimates should not be described as statistical superiority.

HEST task breakdown

HEST uses PCA to 256 dimensions followed by ridge regression for a 50-gene panel. The metric is Pearson correlation averaged over official splits.

HEST task	Mettle	H-optimus-0 (CLS+mean)
IDC	0.6104	0.6106
PRAD	0.3789	0.36215
PAAD	0.5309	0.5106
SKCM	0.6745	0.6614
COAD	0.3331	0.3089
READ	0.2372	0.2401
CCRCC	0.2552	0.2919
LUNG	0.5777	0.5754
LYMPH_IDC	0.2751	0.2665
Unweighted mean	0.4303	0.4253

The H-optimus-0 column is our own evaluation of the unmodified model using the same CLS+mean representation and protocol. Published H-optimus-0 HEST results use the conventional CLS-only embedding and average 0.4150, which would make this margin appear larger.

Usage

Install the dependencies listed in requirements.txt, then load the image processor and model through the standard Transformers auto classes.

import torch
from PIL import Image
from transformers import AutoImageProcessor, AutoModel

model_id = "slideflow-labs/Mettle"
processor = AutoImageProcessor.from_pretrained(model_id)
model = AutoModel.from_pretrained(
    model_id,
    trust_remote_code=True,
).eval().cuda()

image = Image.open("tile.png").convert("RGB")
pixel_values = processor(
    images=image,
    return_tensors="pt",
)["pixel_values"].cuda()

with torch.inference_mode():
    # Representation used for the public benchmarks: (batch, 3072)
    cls_mean = model.encode(pixel_values, feature_view="cls_mean")

    # Conventional compatibility representation: (batch, 1536)
    cls = model.encode(pixel_values, feature_view="cls")

Choosing a representation

The reported public benchmarks use the 3,072-dimensional CLS+mean representation. The loader also exposes the 1,536-dimensional CLS embedding for compatibility; CLS can also be stronger on some endpoints.

Intended use

Mettle is intended for research involving histopathology representation learning, feature extraction, model development, and robustness analysis. Users should validate the model on the intended tissue, acquisition hardware, population, and downstream endpoint.

Do not use Mettle as a standalone diagnostic system. Appropriate human oversight, validation, and compliance requirements remain necessary.

Limitations

• Performance can vary with tissue type, preparation, stain, scanner, compression, magnification, and downstream protocol.
• Scanner consistency does not guarantee preservation of every biological signal or subgroup.
• The two output representations are endpoint-dependent; neither dominates every benchmark.
• PathoROB Robustness Index is reported without uncertainty estimates.
• Benchmark results do not establish clinical efficacy or safety.
• The parent model's limitations and license obligations continue to apply.

References

• H-optimus-0 model card
• Towards robust foundation models for digital pathology (PathoROB)
• HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis
• eva: Evaluation framework for pathology foundation models

Citation

An arXiv preprint is forthcoming.

License

Mettle is licensed under CC-BY-NC-ND-4.0, and is available for academic, non-commercial use.