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README.md

@@ -1,29 +1,71 @@
 ---
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': begnin
-          '1': malignant
-  splits:
-  - name: train
-    num_bytes: 3675514.0
-    num_examples: 160
-  - name: validation
-    num_bytes: 452027.0086991301
-    num_examples: 40
-  download_size: 4290812
-  dataset_size: 4127541.0086991303
+license: mit
 ---
-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 (not pre-split)
+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\Lymphnode_Cancer_Biopsy_100k testsize=0.1 savemodel=C:\path\to\models\mymodel.classify log=C:\path\to\log_files stopwhendone
+```
+
+Explanation of Command:
+- `C:\PrismRCL\PrismRCL.exe`: Path to the PrismRCL executable for classification
+- `chisquared`: Specifies Chi-squared as the training evaluation method
+- `rclticks=10`: Sets the number of RCL iterations during training to 10
+- `boxdown=0`: Configuration parameter for training behavior
+- `data=C:\path\to\Lymphnode_Cancer_Biopsy_100k`: Path to the complete dataset for Lymphnode Cancer Biopsy classification
+- `testsize=0.1`: Specifies that 10% of the data should be used for testing
+- `savemodel=C:\path\to\models\mymodel.classify`: Path to save the resulting trained model
+- `log=C:\path\to\log_files`: Directory path for storing log files of the training process
+- `stopwhendone`: Instructs PrismRCL to end the session once training is complete
+
+## 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