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license: mit |
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--- |
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# Lymphnode Cancer Biopsy Dataset (100k) |
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## Overview |
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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. |
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## Dataset Structure |
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The dataset is organized into the following structure: |
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``` |
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{dataset_folder_name}/ |
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train_data/ |
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benign/ |
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sample_0.png |
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sample_1.png |
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... |
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malignant/ |
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sample_0.png |
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sample_1.png |
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... |
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test_data/ |
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benign/ |
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sample_0.png |
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sample_1.png |
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... |
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malignant/ |
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sample_0.png |
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sample_1.png |
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... |
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``` |
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**Note**: All image file names must be unique across all class folders. |
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## Features |
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- **Image Data**: Each file contains a biopsy image of lymphnode cancer tissue. |
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- **Classes**: There are two classes, each represented by a separate folder based on the type of tissue (benign or malignant). |
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## Usage (not pre-split) |
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Here is an example of how to load the dataset using PrismRCL: |
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```bash |
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C:\PrismRCL\PrismRCL.exe chisquared rclticks=10 boxdown=0 data=C:\path\to\Lymphnode_Cancer_Biopsy_100k testsize=0.1 savemodel=C:\path\to\models\mymodel.classify log=C:\path\to\log_files stopwhendone |
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``` |
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Explanation of Command: |
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- `C:\PrismRCL\PrismRCL.exe`: Path to the PrismRCL executable for classification |
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- `chisquared`: Specifies Chi-squared as the training evaluation method |
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- `rclticks=10`: Sets the number of RCL iterations during training to 10 |
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- `boxdown=0`: Configuration parameter for training behavior |
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- `data=C:\path\to\Lymphnode_Cancer_Biopsy_100k`: Path to the complete dataset for Lymphnode Cancer Biopsy classification |
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- `testsize=0.1`: Specifies that 10% of the data should be used for testing |
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- `savemodel=C:\path\to\models\mymodel.classify`: Path to save the resulting trained model |
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- `log=C:\path\to\log_files`: Directory path for storing log files of the training process |
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- `stopwhendone`: Instructs PrismRCL to end the session once training is complete |
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## License |
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This dataset is licensed under the Creative Commons Attribution 4.0 International License. See the LICENSE file for more details. |
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## Original Source |
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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. |
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## Additional Information |
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The data values have been prepared to ensure compatibility with PrismRCL. No normalization is required as of version 2.4.0. |
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## Citations |
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If you use this dataset in your research, please cite the following papers: |
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1. Veeling, B. S., Linmans, J., Winkens, J., Cohen, T., & Welling, M. (2018). Rotation Equivariant CNNs for Digital Pathology. arXiv preprint arXiv:1806.03962. |
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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 |
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