metadata

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:

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. 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:

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