Update README.md

README.md

@@ -1,10 +1,10 @@
 ---
 license: apache-2.0
 tags:
-- medical
-- 3D medical image caption
 - image-text pair
-- medical report
+- image-captioning
+- 3D medical images
+- medical reports
 size_categories:
 - 100K<n<1M
 ---
@@ -16,26 +16,41 @@ Large-scale 3D medical multi-modal dataset - Image-Text Pair Dataset (M3D-Cap)
 ### Dataset Introduction
 Medical institutions, such as hospitals, store vast amounts of multi-modal data, 
 including medical images and diagnostic reports. 
-However, disclosing these multi-modal datasets involving patient data faces challenges due to sensitivity and privacy concerns. 
-To circumvent these limitations, we collected medical images and reports from publicly accessible professional medical websites [Radiopaedia](https://radiopaedia.org/). 
-Specifically, each patient case in our dataset includes multiple images and corresponding reports, which experts from the Radiopaedia platform meticulously review. 
-Given the crucial role of 3D CT in medical image analysis, particularly in the diagnosis, localization, and measurement of systemic lesions, 
-we focus on 3D CT data. We successfully constructed the largest-scale 3D medical image-text paired dataset, M3D-Cap, 
-comprising 120K pairs of image-text data. Overall, it is divided into two data folders named ct_case and ct_quizze. 
-ct_quizze is used for medical exams and has higher quality. Each folder contains some image folders and one text file. 
-The image folders contain multiple 2D slices of 3D images, while the text files provide English reports describing the corresponding 3D images, 
-including types of abnormalities and lesions. M3D_Cap.json provides the split scheme.
-
+However, due to the sensitivity and privacy concerns associated with patient data, 
+publicly releasing these multimodal datasets poses challenges. 
+To overcome these limitations, we collected medical images and reports from the publicly 
+accessible professional medical website [Radiopaedia](https://radiopaedia.org/). 
+Specifically, each patient case in our dataset consists of multiple 3D images and corresponding reports, 
+which experts on the Radiopaedia platform have meticulously reviewed. 
+Given the critical role of 3D CT in medical image analysis, particularly in the diagnosis, 
+localization, and measurement of systemic lesions, we focused on 3D CT data and successfully 
+built the largest-scale 3D medical image-text paired dataset, named M3D-Cap, comprising 120K image-text pairs.
+
+The dataset is divided into two main folders named ct_case and ct_quizze. 
+The ct_quizze folder is intended for medical exams and exhibits higher quality. 
+Each folder contains subfolders for images and texts. 
+The image folders contain multiple 2D slices of 3D images, 
+and the text files provide English report descriptions corresponding to the 3D images, 
+including anomaly types, lesion locations, etc.
+
+- **M3D_Cap.json**: Provides the dataset split.
+- **data_examples**: Provides examples of 24 sets of 3D images and text data.
+- **M3D_Cap**: Provides the complete dataset, please download this folder.
+- **m3d_cap_data_prepare.py**: Provides data preprocessing code, including image normalization,
+stack 3D images from 2D slices, image cropping, and effective text extraction.
+
+Based on the image-text pairs in the M3D-Cap dataset, we created the M3D-VQA (Visual Question Answering) dataset.
+Please refer to the [link](https://www.modelscope.cn/datasets/GoodBaiBai88/M3D-VQA).
 
 ### Supported Tasks
-M3D-Cap supports various image-text multimodal tasks in 3D medical scenarios, 
-including image-text retrieval, report generation, and image generation.
+M3D-Cap supports multimodal tasks in 3D medical scenarios such as image-text retrieval, 
+report generation, and image generation.
 
 ## Dataset Format and Structure
 
 ### Data Format
 <pre>
-    M3D_Seg/
+    M3D_Cap/
         ct_case/
             000006/
                 Axial_non_contrast/
@@ -56,23 +71,36 @@ including image-text retrieval, report generation, and image generation.
 </pre>
 
 ### Dataset Download
+
+The total size of the dataset is approximately 978G. 
+Please note that the contents of the data_examples folder are only examples and do not need to be downloaded. 
+The complete dataset is located in the M3D_Cap folder.
+
 #### Clone with HTTP
 ```bash
-git clone 
+git clone https://huggingface.co/datasets/GoodBaiBai88/M3D-Cap
+```
+
+#### SDK Download
+```bash
+from datasets import load_dataset
+dataset = load_dataset("GoodBaiBai88/M3D-Cap")
 ```
+
 #### Manual Download
-Download all files from the dataset manually, which can be done using batch download tools. 
-Note: Due to the large size of the overall dataset, it is divided into subfiles of 20G each. 
-After downloading all files, extract them together to obtain the complete data.
+Manually download all files from the dataset, and we recommend using a batch download tool.
 
 
 ### Dataset Loading Method
 #### 1. Preprocessing
-Combine slices under each folder in the dataset to form 3D images and name them according to the image file names (retain plane and phase information), 
-saving them as npy files. Filter the text reports in the dataset to obtain high-quality descriptions.
+Preprocess the dataset according to m3d_cap_data_prepare.py, including: 
+stack 3D images from 2D slices in each folder of the dataset and name them with the image file name 
+(retaining plane and phase information), saving as `npy` files, 
+image normalization and cropping, and filtering and extracting high-quality descriptions 
+from the text reports in the dataset.
 
 #### 2. Build Dataset 
-We provide sample code for building the dataset
+We provide examples for building the Dataset:
 
 ```python
 class CapDataset(Dataset):
@@ -194,9 +222,11 @@ class CapDataset(Dataset):
 
 
 ### Data Splitting
-The entire dataset is split into
-‘train, validation, test100, test500, test1k, and test’ using a JSON file. 
-considering testing costs, we provide different quantities of test samples from 100 to 2k, with the number of ‘test‘ being 2k.
+The entire dataset is split using a JSON file and can be divided into 
+`train, validation, test100, test500, test1k, test`, where the test subset contains 2k samples. 
+Considering testing costs, we provide test sets with different sample sizes, 
+including 100, 500, 1k, and 2k samples.
+
 
 ## Dataset Copyright Information