Improve dataset card: Add task category, paper/code links, and LMDB sample usage

This PR enhances the `CPathPatchFeature` dataset card by:

* Adding `task_categories: ['image-feature-extraction']` to the metadata, which accurately reflects the dataset's purpose.
* Including direct links to the associated paper ([Revisiting End-to-End Learning with Slide-level Supervision in Computational Pathology](https://huggingface.co/papers/2506.02408)) and the GitHub repository ([https://github.com/DearCaat/E2E-WSI-ABMILX](https://github.com/DearCaat/E2E-WSI-ABMILX)) at the top of the content for improved discoverability.
* Adding a "Sample Usage" section with a Python code snippet, directly sourced from the project's GitHub README, demonstrating how to load data from an LMDB dataset.

These updates provide more comprehensive information and make the dataset easier to understand and utilize for researchers.

README.md

@@ -1,16 +1,21 @@
 ---
-license: apache-2.0
 language:
 - en
+license: apache-2.0
+size_categories:
+- 100B<n<1T
 tags:
 - medical
 - pathology
-size_categories:
-- 100B<n<1T
+task_categories:
+- image-feature-extraction
 ---
 
 # CPathPatchFeature: Pre-extracted WSI Features for Computational Pathology
 
+Paper: [Revisiting End-to-End Learning with Slide-level Supervision in Computational Pathology](https://huggingface.co/papers/2506.02408)
+Code: [https://github.com/DearCaat/E2E-WSI-ABMILX](https://github.com/DearCaat/E2E-WSI-ABMILX)
+
 ## 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.
@@ -73,6 +78,58 @@ Then, clone the dataset repository:
 git clone https://huggingface.co/datasets/Dearcat/CPathPatchFeature
 ```
 
+## Sample Usage
+
+Here is an example of loading data from an LMDB dataset, as provided in the GitHub repository:
+
+```python
+import lmdb
+import torch
+import pickle
+from datasets.utils import imfrombytes # Ensure this utility function is correctly referenced
+
+slide_name = "xxxx"  # Example slide name
+path_to_lmdb = "YOUR_PATH_TO_LMDB_FILE" # e.g., "/path/to/my_dataset_256_level0.lmdb"
+
+# Open LMDB dataset
+env = lmdb.open(path_to_lmdb, subdir=False, readonly=True, lock=False,
+                readahead=False, meminit=False, map_size=100 * (1024**3))
+
+with env.begin(write=False) as txn:
+    # Get patch count for the slide
+    pn_dict = pickle.loads(txn.get(b'__pn__'))
+    if slide_name not in pn_dict:
+        raise ValueError(f"Slide ID {slide_name} not found in LMDB metadata.")
+    num_patches = pn_dict[slide_name]
+
+    # Generate patch IDs
+    patch_ids = [f"{slide_name}-{i}" for i in range(num_patches)]
+
+    # Allocate memory for patches (adjust dimensions and dtype as needed)
+    # Assuming patches are 224x224, 3 channels, and will be normalized later
+    patches_tensor = torch.empty((len(patch_ids), 3, 224, 224), dtype=torch.float32)
+
+    # Load and decode data into torch.tensor
+    for i, key_str in enumerate(patch_ids):
+        patch_bytes = txn.get(key_str.encode('ascii'))
+        if patch_bytes is None:
+            print(f"Warning: Key {key_str} not found in LMDB.")
+            continue
+        # Assuming the stored value is pickled image bytes
+        img_array = imfrombytes(pickle.loads(patch_bytes).tobytes()) # Or .tobytes() if it's already bytes
+        patches_tensor[i] = torch.from_numpy(img_array.transpose(2, 0, 1)) # HWC to CHW
+
+# Normalize the data (example using ImageNet stats)
+# Ensure values are in [0, 255] before this normalization if they aren't already
+mean = torch.tensor([0.485, 0.456, 0.406]).view((1, 3, 1, 1)) * 255.0
+std = torch.tensor([0.229, 0.224, 0.225]).view((1, 3, 1, 1)) * 255.0
+
+# If your patches_tensor is already in [0,1] range, remove * 255.0 from mean/std
+# If your patches_tensor is uint8 [0,255], convert to float first: patches_tensor.float()
+patches_tensor = (patches_tensor.float() - mean) / std
+
+env.close()
+```
 
 ### Citation
 This dataset has been used in the following publications. If you find it useful for your research, please consider citing them: