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TIACentre/
resnet50-kather100k

Name: resnet50-kather100k
Availability: InStock

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ResNet-50 Trained on Kather100K (via TIAToolbox)

This model is a ResNet-50 convolutional neural network trained on the Kather100K dataset, which comprises 100,000 histological images of human colorectal cancer and healthy tissue. The model was developed using the TIAToolbox, an end-to-end library for advanced tissue image analytics.

Model Details

Architecture: ResNet-50
Parameters: 23.6 million
Input Size: 224 × 224 × 3
Dataset: Kather100K (also known as NCT-CRC-HE)
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Source: TIAToolbox

Dataset

The Kather100K dataset consists of 100,000 non-overlapping image patches (224 × 224 pixels) extracted from histological images of human colorectal cancer and healthy tissue. The dataset includes nine tissue classes:

Tumor epithelium
Simple stroma
Complex stroma
Immune cells
Debris
Mucus
Adipose tissue
Background
Muscle

Performance

On the Kather100K dataset, the ResNet-50 model achieved an F1-score of 0.989, indicating high accuracy in classifying histological tissue types.

Usage

Inference with TIAToolbox

python

from tiatoolbox.models.architecture import get_pretrained_model

# Load the pretrained ResNet-50 model trained on Kather100K
model = get_pretrained_model(pretrained_model='resnet50-kather100k')

TIACentre/resnet50-kather100k