---
license: mit
---
# Lymphnode Cancer Biopsy Dataset (100k)

## Overview
This dataset contains biopsy images of lymphnode cancer tissues, divided into two classes: benign and malignant. Each sample is stored in a separate image file, organized into respective class folders. The dataset is structured to be compatible with Lumina AI's Random Contrast Learning (RCL) algorithm via the PrismRCL application or API.

## Dataset Structure
The dataset is organized into the following structure:

```
{dataset_folder_name}/
    train_data/
        benign/
            sample_0.png
            sample_1.png
            ...
        malignant/
            sample_0.png
            sample_1.png
            ...
    test_data/
        benign/
            sample_0.png
            sample_1.png
            ...
        malignant/
            sample_0.png
            sample_1.png
            ...
```

**Note**: All image file names must be unique across all class folders.

## Features
- **Image Data**: Each file contains a biopsy image of lymphnode cancer tissue.
- **Classes**: There are two classes, each represented by a separate folder based on the type of tissue (benign or malignant).

## Usage
Here is an example of how to load the dataset using PrismRCL:

```bash
C:\PrismRCL\PrismRCL.exe chisquared rclticks=10 boxdown=0 data=C:\path\to\{dataset_folder_name}\train_data testdata=C:\path\to\{dataset_folder_name}\test_data savemodel=C:\path\to\models\mymodel.classify log=C:\path\to\log_files stopwhendone
```

**Explanation:**
- `C:\PrismRCL\PrismRCL.exe`: classification application
- `chisquared`: training evaluation method
- `rclticks=10`: RCL training parameter
- `boxdown=0`: RCL training parameter
- `data=C:\path\to\{dataset_folder_name}\train_data`: path to training data
- `testdata=C:\path\to\{dataset_folder_name}\test_data`: path to testing data
- `savemodel=C:\path\to\models\mymodel.classify`: path to save resulting model
- `log=C:\path\to\log_files`: path to logfiles
- `stopwhendone`: ends the PrismRCL session when training is done

## License
This dataset is licensed under the Creative Commons Attribution 4.0 International License. See the LICENSE file for more details.

## Original Source
This dataset was originally sourced from the [GitHub Repository](https://github.com/basveeling/pcam). Please cite the original source if you use this dataset in your research or applications.

## Additional Information
The data values have been prepared to ensure compatibility with PrismRCL. No normalization is required as of version 2.4.0.

## Citations
If you use this dataset in your research, please cite the following papers:

1. Veeling, B. S., Linmans, J., Winkens, J., Cohen, T., & Welling, M. (2018). Rotation Equivariant CNNs for Digital Pathology. arXiv preprint arXiv:1806.03962.

2. Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., ... & the CAMELYON16 Consortium. (2017). Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA, 318(22), 2199–2210. https://doi.org/10.1001/jama.2017.14585