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NCT-CRC-HE Tissue Classifier (ResNet50)

Image generationUnavailable
by CommunityModel ID: nct-crc-he-resnet50-tissue

ResNet50 fine-tuned on the NCT-CRC-HE-45K colorectal histology dataset. It sorts an H&E tissue patch into nine classes: adipose (ADI), background (BACK), debris (DEB), lymphocytes (LYM), mucus (MUC), smooth muscle (MUS), normal mucosa (NORM), stroma (STR) and tumor epithelium (TUM). Research use only, not a diagnostic device.

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Status
Unavailable
Input → output
Text + Image → Image
Developer
Community
Updated
September 23, 2026

NCT-CRC-HE Tissue Classifier (ResNet50) is currently unavailable

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Playground

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Input & output

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Try NCT-CRC-HE Tissue Classifier (ResNet50)

H&E colorectal tissue tile to classify

Output
Your image appears here.

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About NCT-CRC-HE Tissue Classifier (ResNet50)

TL;DRAs of September 23, 2026

NCT-CRC-HE Tissue Classifier (ResNet50) is a model by Community in the Image generation category. NCT-CRC-HE Tissue Classifier (ResNet50) is currently not available on Railwail.

A Hugging Face transformers image-classification model that fine-tunes ResNet50 on the 45,000-patch validation split of the NCT-CRC-HE colorectal histology dataset. The label set matches the standard Kather nine-tissue taxonomy, so it is useful for tumor-versus-stroma mapping, tissue quantification and as a teaching example of patch-level histopathology classification. It runs through the hosted image-classification pipeline and returns class probabilities for one input patch. Not for clinical use.
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Pricing

Currently unavailable. There is no price for this model at the moment, so it cannot be run.

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API

Call NCT-CRC-HE Tissue Classifier (ResNet50) with your Railwail API key. Use this model ID in the request:
nct-crc-he-resnet50-tissueAPI documentationGet an API key

No verified API example

The public API passes a different input format than this model needs. Use the playground above.

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Specifications

Model ID
nct-crc-he-resnet50-tissue
Developer
Community
Category
Image generation
Input
Text, Image
Output
Image
Catalog entry updated
September 23, 2026

Input parameters

Inputs and settings from the model's input schema. The example in the API section shows which of them the API accepts.

  • imagerequired

    H&E colorectal tissue tile to classify

    Type: Text
    Default: –
    Allowed values: –

Tags

  • huggingface
  • medical
  • research
  • not-diagnostic
  • pathology
  • histopathology
  • colorectal
  • nct-crc
  • resnet50
  • image-classification
  • image
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Use cases

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Frequently asked questions

What is NCT-CRC-HE Tissue Classifier (ResNet50)?

NCT-CRC-HE Tissue Classifier (ResNet50) is a model by Community in the Image generation category. It is listed on Railwail but cannot be run at the moment.

How much does NCT-CRC-HE Tissue Classifier (ResNet50) cost on Railwail?

NCT-CRC-HE Tissue Classifier (ResNet50) cannot be run on Railwail at the moment, so there is no current price. Available alternatives with prices are listed further down this page.

Which settings does NCT-CRC-HE Tissue Classifier (ResNet50) support?

According to its input schema, NCT-CRC-HE Tissue Classifier (ResNet50) knows these parameters: image.

How fast is NCT-CRC-HE Tissue Classifier (ResNet50)?

There are not enough measured runs of NCT-CRC-HE Tissue Classifier (ResNet50) on Railwail yet to state a run time. It depends on the input, the settings and the load at the provider.

Is NCT-CRC-HE Tissue Classifier (ResNet50) better than FLUX 1.1 Pro?

That depends on the task. NCT-CRC-HE Tissue Classifier (ResNet50) (Community) and FLUX 1.1 Pro (Black Forest Labs) are both models in the Image generation category. The comparison page shows their prices and specifications side by side.

Compare NCT-CRC-HE Tissue Classifier (ResNet50) and FLUX 1.1 Pro

Can NCT-CRC-HE Tissue Classifier (ResNet50) use an image as input?

Yes. Send the image in the image parameter.

Can I use NCT-CRC-HE Tissue Classifier (ResNet50) right now?

Currently unavailable. The page stays online; available alternatives from the same category are listed further down.

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