Update README.md

README.md

@@ -1,3 +1,111 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+tags:
+- medical
+- pathology
+size_categories:
+- 100B<n<1T
+---
+
+# CPathPatchFeature: Pre-extracted WSI Features for Computational Pathology
+
+## Dataset Summary
+
+This dataset provides a comprehensive collection of pre-extracted features from Whole Slide Images (WSIs) for various cancer types, designed to facilitate research in computational pathology. The features are extracted using multiple state-of-the-art encoders, offering a rich resource for developing and evaluating Multiple Instance Learning (MIL) models and other deep learning architectures.
+
+
+The repository contains features for the following public datasets:
+- **PANDA**: Prostate cANcer graDe Assessment
+- **TCGA-BRCA**: Breast Cancer in TCGA
+- **TCGA-NSCLC**: Non-Small Cell Lung Cancer in TCGA
+- **TCGA-BLCA**: Bladder Cancer in TCGA
+- **CAMELYON**: Cancer Metastases in Lymph Nodes
+- **CPTAC-NSCLC**: Non-Small Cell Lung Cancer in CPTAC
+
+## Dataset Structure
+
+The features for each WSI dataset are organized into subdirectories. Each subdirectory contains the features extracted by a specific encoder, along with the corresponding patch coordinates.
+
+### Feature Encoders
+The following encoders were used to generate the features:
+- **UNI**: A vision-language pretrained model for pathology ([UNI by Chen et al.](https://www.nature.com/articles/s41591-024-02857-3)).
+- **CHIEF**: A feature extractor based on self-supervised learning for pathology ([CHIEF by Wang et al.](https://www.nature.com/articles/s41586-024-07894-z)).
+- **GIGAP**: A Giga-Pixel vision model for pathology ([GigaPath by Xu et al.](https://www.nature.com/articles/s41586-024-07441-w)).
+- **R50**: A ResNet-50 model pre-trained on ImageNet.
+
+Some data may not be fully organized yet. If you have specific needs or questions, please feel free to open an issue in the community tab.
+
+## How to Use
+
+You can load and access the dataset using the Hugging Face `datasets` library or by cloning the repository with Git LFS.
+
+### Using the `datasets` Library
+
+To load the data, you can use the following Python code:
+
+```python
+from datasets import load_dataset
+
+# Load a specific subset (e.g., PANDA)
+# Note: You may need to specify the data files manually depending on the configuration.
+# Example for a hypothetical configuration named 'panda'
+# ds = load_dataset("your-username/CPathPatchFeature", name="panda")
+
+# For datasets with this structure, it's often easier to download and access files directly.
+# We recommend using Git LFS for a complete download.
+````
+
+*Note: Due to the heterogeneous structure (mixed zipped and unzipped files), direct loading with `load_dataset` might be complex. The recommended approach is to clone the repository.*
+
+### Using Git LFS
+
+First, ensure you have Git LFS installed and configured:
+
+```bash
+git lfs install
+```
+
+Then, clone the dataset repository:
+
+```bash
+git clone [https://huggingface.co/datasets/your-username/CPathPatchFeature](https://huggingface.co/datasets/your-username/CPathPatchFeature)
+```
+
+Replace `"your-username/CPathPatchFeature"` with the actual repository path on Hugging Face.
+
+### Citation
+This dataset has been used in the following publications. If you find it useful for your research, please consider citing them:
+
+```bibtex
+@misc{tang2025revisitingdatachallengescomputational,
+      title={Revisiting Data Challenges of Computational Pathology: A Pack-based Multiple Instance Learning Framework}, 
+      author={Wenhao Tang and Heng Fang and Ge Wu and Xiang Li and Ming-Ming Cheng},
+      year={2025},
+      eprint={2509.20923},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={[https://arxiv.org/abs/2509.20923](https://arxiv.org/abs/2509.20923)}, 
+}
+
+@misc{tang2025multipleinstancelearningframework,
+      title={Multiple Instance Learning Framework with Masked Hard Instance Mining for Gigapixel Histopathology Image Analysis}, 
+      author={Wenhao Tang and Sheng Huang and Heng Fang and Fengtao Zhou and Bo Liu and Qingshan Liu},
+      year={2025},
+      eprint={2509.11526},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={[https://arxiv.org/abs/2509.11526](https://arxiv.org/abs/2509.11526)}, 
+}
+
+@misc{tang2025revisitingendtoendlearningslidelevel,
+      title={Revisiting End-to-End Learning with Slide-level Supervision in Computational Pathology}, 
+      author={Wenhao Tang and Rong Qin and Heng Fang and Fengtao Zhou and Hao Chen and Xiang Li and Ming-Ming Cheng},
+      year={2025},
+      eprint={2506.02408},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={[https://arxiv.org/abs/2506.02408](https://arxiv.org/abs/2506.02408)}, 
+}
+```