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NCT-CRC-HE Tissue Classifier (ResNet50)

huggingface
Image Generation

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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TL;DR·Last updated June 24, 2026

NCT-CRC-HE Tissue Classifier (ResNet50) is image generation AI model from huggingface, priced at €0.000 per 1M input tokens with a unknown context window.

About this model

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

Price per Generation
Per generation€1.00

API Integration

Use our OpenAI-compatible API to integrate NCT-CRC-HE Tissue Classifier (ResNet50) into your application.

Install
npm install railwail
JavaScript / TypeScript
import railwail from "railwail";

const rw = railwail("YOUR_API_KEY");

const images = await rw.run("nct-crc-he-resnet50-tissue", "A beautiful sunset over Tokyo");
console.log(images[0].url);

// Or use the image() method for full control
const res = await rw.image("nct-crc-he-resnet50-tissue", "A cat in space", {
  size: "1024x1024",
  n: 1,
});
console.log(res.data[0].url);
Specifications
Price
€1.00
Developer
huggingface
Category
Image Generation
Supported Formats
image
Tags
huggingface
medical
research
not-diagnostic
pathology
histopathology
colorectal
nct-crc
resnet50
image-classification
image

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