Update README.md

README.md

@@ -1,3 +1,226 @@
 ---
 license: apache-2.0
+tags:
+- medical
+- 3D medical image caption
+- image-text pair
+- medical report
+size_categories:
+- 100K<n<1M
 ---
+
+## Dataset Description
+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. 
+Specifically, each patient case in our dataset includes multiple images along with their 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 a largest-scale 3D medical image-text paired dataset, named 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.
+
+
+### Supported Tasks
+M3D-Cap supports various image-text multimodal tasks in 3D medical scenarios, 
+including image-text retrieval, report generation, and image generation.
+
+## Dataset Format and Structure
+
+### Data Format
+<pre>
+    M3D_Seg/
+        ct_case/
+            000006/
+                Axial_non_contrast/
+                    0.jpeg
+                    1.jpeg
+                    ......
+                text.txt
+            ......
+        ct_quizze/
+            000007/
+                Axial_non_contrast/
+                    0.png
+                    1.png
+                    ......
+                text.txt
+            ......
+        ......
+</pre>
+
+### Dataset Download
+#### Clone with HTTP
+```bash
+git clone 
+```
+#### Manual Download
+Download all files from the dataset file 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.
+
+
+### 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.
+
+#### 2. Build Dataset 
+We provide sample code for building the dataset
+
+```python
+class CapDataset(Dataset):
+    def __init__(self, args, tokenizer, mode="train"):
+        self.args = args
+        self.data_root = args.data_root
+        self.tokenizer = tokenizer
+        self.mode = mode
+
+        self.image_tokens = "<im_patch>" * args.proj_out_num
+
+        with open(args.cap_data_path, 'r') as file:
+            self.json_file = json.load(file)
+        self.data_list = self.json_file[mode]
+
+        self.caption_prompts = [
+            "Can you provide a caption consists of findings for this medical image?",
+            "Describe the findings of the medical image you see.",
+            "Please caption this medical scan with findings.",
+            "What is the findings of this image?",
+            "Describe this medical scan with findings.",
+            "Please write a caption consists of findings for this image.",
+            "Can you summarize with findings the images presented?",
+            "Please caption this scan with findings.",
+            "Please provide a caption consists of findings for this medical image.",
+            "Can you provide a summary consists of findings of this radiograph?",
+            "What are the findings presented in this medical scan?",
+            "Please write a caption consists of findings for this scan.",
+            "Can you provide a description consists of findings of this medical scan?",
+            "Please caption this medical scan with findings.",
+            "Can you provide a caption consists of findings for this medical scan?"
+        ]
+
+        train_transform = mtf.Compose(
+            [
+                mtf.RandRotate90(prob=0.5, spatial_axes=(1, 2)),
+                mtf.RandFlip(prob=0.10, spatial_axis=0),
+                mtf.RandFlip(prob=0.10, spatial_axis=1),
+                mtf.RandFlip(prob=0.10, spatial_axis=2),
+                mtf.RandScaleIntensity(factors=0.1, prob=0.5),
+                mtf.RandShiftIntensity(offsets=0.1, prob=0.5),
+
+                mtf.ToTensor(dtype=torch.float),
+            ]
+        )
+
+        val_transform = mtf.Compose(
+                [
+                    mtf.ToTensor(dtype=torch.float),
+                ]
+            )
+        set_track_meta(False)
+
+        if mode == 'train':
+            self.transform = train_transform
+        elif mode == 'validation':
+            self.transform = val_transform
+        elif mode == 'test':
+            self.transform = val_transform
+
+    def __len__(self):
+        return len(self.data_list)
+
+    def __getitem__(self, idx):
+        max_attempts = 100
+        for _ in range(max_attempts):
+            try:
+                data = self.data_list[idx]
+                image_path = data["image"]
+                image_abs_path = os.path.join(self.data_root, image_path)
+                image = np.load(image_abs_path)  # nomalized 0-1, C,D,H,W
+                image = self.transform(image)
+
+                text_path = data["text"]
+                text_abs_path = os.path.join(self.data_root, text_path)
+                with open(text_abs_path, 'r') as text_file:
+                    raw_text = text_file.read()
+                answer = raw_text
+
+                prompt_question = random.choice(self.caption_prompts)
+
+                question = self.image_tokens + prompt_question
+
+                text_tensor = self.tokenizer(
+                    question + ' ' + answer, max_length=self.args.max_length, truncation=True, padding="max_length", return_tensors="pt"
+                )
+
+                input_id = text_tensor["input_ids"][0]
+                attention_mask = text_tensor["attention_mask"][0]
+
+                valid_len = torch.sum(attention_mask)
+                if valid_len < len(input_id):
+                    input_id[valid_len] = self.tokenizer.eos_token_id
+
+                question_tensor = self.tokenizer(
+                    question, max_length=self.args.max_length, truncation=True, padding="max_length", return_tensors="pt"
+                )
+                question_len = torch.sum(question_tensor["attention_mask"][0])
+
+                label = input_id.clone()
+                label[label == self.tokenizer.pad_token_id] = -100
+                label[:question_len] = -100
+
+                ret = {
+                    'image': image,
+                    'input_id': input_id,
+                    'label': label,
+                    'attention_mask': attention_mask,
+                    'question': question,
+                    'answer': answer,
+                    'question_type': "Caption",
+                }
+                return ret
+
+            except Exception as e:
+                print(f"Error in __getitem__ at index {idx}: {e}")
+                idx = random.randint(0, len(self.data_list) - 1)
+```
+
+
+### 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.
+
+## Dataset Copyright Information
+
+All images and reports involved in this dataset are publicly available data. 
+For detailed copyright information, please refer to the corresponding links.
+
+## Citation
+If you use this dataset, please cite the following works:
+
+```BibTeX
+@misc{bai2024m3d,
+      title={M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models}, 
+      author={Fan Bai and Yuxin Du and Tiejun Huang and Max Q. -H. Meng and Bo Zhao},
+      year={2024},
+      eprint={2404.00578},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+@misc{du2024segvol,
+      title={SegVol: Universal and Interactive Volumetric Medical Image Segmentation}, 
+      author={Yuxin Du and Fan Bai and Tiejun Huang and Bo Zhao},
+      year={2024},
+      eprint={2311.13385},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```