Update README.md

README.md

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----
-license: cc0-1.0
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': '0'
-          '1': '1'
-  splits:
-  - name: train
-    num_bytes: 1439568612.0
-    num_examples: 249300
-  - name: validation
-    num_bytes: 79756681.5
-    num_examples: 13850
-  - name: test
-    num_bytes: 80391648.6
-    num_examples: 13850
-  download_size: 1678110616
-  dataset_size: 1599716942.1
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
----
+---
+license: cc0-1.0
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': '0'
+          '1': '1'
+  splits:
+  - name: train
+    num_bytes: 1439568612
+    num_examples: 249300
+  - name: validation
+    num_bytes: 79756681.5
+    num_examples: 13850
+  - name: test
+    num_bytes: 80391648.6
+    num_examples: 13850
+  download_size: 1678110616
+  dataset_size: 1599716942.1
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+  - split: test
+    path: data/test-*
+task_categories:
+- image-classification
+language:
+- en
+tags:
+- medical imaging
+- histopathology
+- breast cancer
+- classification
+pretty_name: Breast Histopathology Images
+size_categories:
+- 10K<n<100K
+---
+
+## Dataset Card for Breast Histopathology Images
+
+### Dataset Overview
+
+**Breast Histopathology Images** is a dataset containing high-resolution images of breast cancer specimens, specifically focusing on Invasive Ductal Carcinoma (IDC). The dataset is used for developing models to automatically detect and grade the aggressiveness of breast cancer based on histopathological images.
+
+### Context
+
+Invasive Ductal Carcinoma (IDC) is the most common subtype of breast cancer. Pathologists often need to identify regions containing IDC to assign an aggressiveness grade to whole mount samples. This dataset provides a collection of patches extracted from whole mount slides to assist in training models for automatic IDC detection and classification.
+
+### Content
+
+- **Images**: 162 whole mount slide images of breast cancer specimens scanned at 40x magnification.
+- **Patches**: 277,524 patches of size 50x50 pixels extracted from the whole mount images.
+  - **IDC Negative**: 198,738 patches
+  - **IDC Positive**: 78,786 patches
+
+
+### Version
+
+This version of the dataset is pre-split into training, validation, and test sets, making it ready for immediate use in machine learning tasks without additional preprocessing.
+
+### Source
+
+The dataset is sourced from Kaggle:
+[Breast Histopathology Images on Kaggle](https://www.kaggle.com/datasets/paultimothymooney/breast-histopathology-images)
+
+### Usage
+
+This dataset is useful for training and evaluating models for histopathological image classification, particularly in the detection of IDC in breast cancer samples.
+
+### License
+
+The dataset is licensed under CC0-1.0, which allows for unrestricted use and distribution.