Create README.md

README.md

@@ -0,0 +1,64 @@
+---
+task_categories:
+- question-answering
+- summarization
+- token-classification
+tags:
+- medical
+---
+
+# Dataset Description
+
+Our OmniFM-Dr framework introduces a multi-task chest x-ray dataset, which is used for 
+the joint training of disease classification, localization, segmentation, and report generation. This dataset comprises various 
+publicly available datasets, such as MIMIC-CXR, VinDr-CXR, and ChestX-Det10. For each image, 
+it can potentially contribute to multiple tasks, such as report generation and classification.
+
+## Dataset Details
+- [**MIMIC:**](https://physionet.org/content/mimic-cxr/2.0.0/)  contains more than 377,110 radiograph images from over 227,835 radiographic studies. Each radiograph is paired with lesion classification and associated radiology report. It
+is used for multi-label classification and report generation tasks.
+- [**Padchest:**](https://arxiv.org/abs/1901.07441) includes 160,840 images obtained from 67,000 patients, covering six different position views.
+Different radiographic findings were labeled and used for the classification task in this study.
+- [**CXR-AL14:**]() is a large-scale dataset for chest X-ray image detection. It has more than 140,000 chest X-ray
+radiographs containing 253,844 bounding boxes in 14 chest abnormal object categories.
+- [**VinDr-CXR:**](https://www.nature.com/articles/s41597-022-01498-w) includes chest radiographs with annotations for the classification of 28 common chest
+diseases. The dataset contains 15,000 CXR scans in the training set. We select eight diseases from the dataset
+along with their corresponding bounding box for the disease localization task.
+- [**ChestX-Det:**](https://arxiv.org/abs/2104.10326) consists of 3,578 images from [NIH ChestXray14](https://arxiv.org/abs/1705.02315) for 13 common disease. We select
+seven diseases from the dataset along with bounding box for the disease localization task.
+- [**CheXmask:**](https://arxiv.org/abs/2307.03293) contains 676,803 lung and heart segmentation masks of chest images from six publicly
+available databases: CANDID-PTX, ChestXray14, Chexpert, MIMIC-CXR, Padchest, and VinDr-CXR. We
+include 224,316 data for training and 10,000 data from ChestXray14 for downstream evaluation.
+- [**SIIM:**](https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation) comes from the SIIM-ACR Pneumothorax Segmentation competition and contains 12,090 images,
+among which approximately 3,000 cases are positive for pneumothorax disease with masks.
+
+## Dataset Structure
+- **MIMIC:**
+  - MIMIC_classification_report-generation_xxx.tsv: is used for classification and report generation tasks. For each row,
+it contains id, report, "label1 && label2", subject_id, study_id, dicom_id.
+  - MIMIC_classification-location_xxx.tsv: is used for location vqa task. For each row,
+it contains id, "label1,severity && label2, severity", subject_id, study_id, dicom_id.
+  - MIMIC_classification-severity_xxx.tsv: is used for severity vqa task. For each row,
+it contains id, "label, location1 & location2", subject_id, study_id, dicom_id.
+- **Padchest:**
+  - Padchest_classification_xxx.tsv: is used for classification task. For each row,
+it contains id, "label1 && label2", subject_id, study_id, dicom_id.
+- **CXR-AL14:**
+  - CXR_AL14_localization_xxx.tsv: is used for locatization and classification tasks. For each row,
+it contains id, label, "x1,y1,x2,y2", subject_id, study_id, dicom_id.
+- **VinDr-CXR:**
+  - VinDr_CXR_localization_xxx.tsv: is used for locatization and classification tasks. For each row,
+it contains id, label, "x1,y1,x2,y2", subject_id, study_id, dicom_id.
+- **ChestX-Det:**
+  - ChestX_Det_localization_xxx.tsv: is used for locatization and classification tasks. For each row,
+it contains id, label, "x1,y1,x2,y2", subject_id, study_id, dicom_id.
+- **CheXmask:**
+  - CheXmask_segmentation_xxx.tsv: is used for segmentation task. For each row,
+it contains id, label, "x1,y1,x2,y2, ..., x30, y30", subject_id, study_id, dicom_id.
+- **SIIM:**
+  - SIIM_segmentation_xxx.tsv: is used for segmentation task. For each row,
+it contains id, label, "x1,y1,x2,y2, ..., x30, y30", subject_id, study_id, dicom_id.
+
+## Dataset Use
+- df = pd.read_csv(f, sep='\t', header=None)
+