---
language:
- en
license: apache-2.0
size_categories:
- 100M<n<1B
task_categories:
- feature-extraction
- text-classification
tags:
- biomedical
- imaging
- computer vision
- tuberculosis
- multimodal
dataset_info:
  features:
  - name: case_id
    dtype: string
  - name: gender
    dtype: string
  - name: age
    dtype: int8
  - name: case_text
    dtype: string
  - name: keywords
    dtype: string
  - name: image_file
    dtype: image
  - name: caption
    dtype: string
  splits:
  - name: train
    num_bytes: 70088819.588
    num_examples: 6284
  download_size: 42809832
  dataset_size: 70088819.588
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# Multimodal Dataset of Tuberculosis Patients including CT and Clinical Case Reports

Zhankai Ye   <br> 
NetID: zy172

## Dataset Summary
This dataset is curated from the original “The MultiCaRe Dataset” to focus on the chest tuberculosis patients. This is a multimodal dataset consisting of lung computed tomography (CT) imaging data and the clinical case records of tuberculosis patients, along with their case keywords, the captions of their CT images, patient_id, gender, and age information. 

## Dataset Sources
- Homepage: https://zenodo.org/records/10079370
- DOI: 10.5281/zenodo.10079370
- Data article: https://www.sciencedirect.com/science/article/pii/S2352340923010351

## Supported Tasks:
This dataset can be utilized for:
- Developing algorithms of the segmentation of chest CT images and the classification of tuberculosis positive or control.
- Developing novel natural language processing (NLP) methods and unsupervised machine learning methods to extract medical terms from clinical notes.

## Languages:
English

## Data Structure and Instance:
The data will follow the structure below:
{
-  `"case_id"`: "PMC10129030_01",
-  `"gender"`: "male", 
-  `"age"`: 62,
-  `"case_text"`: "A 62-year-old man presented with acute dyspnea at rest, requiring high-flow…",
-  `"keywords"`: "["dendriform pulmonary ossification", "lung transplant", "pulmonary fibrosis"]",
-  `"pics_array"`: image
-  `"Caption"`: "coronal. chest CT shows ground-glass and reticular opacities in the dependent…"
}

## Data Fields:
- **case_id (string)**: ID of the patient, created combining the PMC of the article plus a sequential number.
- **gender (string)**: Gender of the patient. It can be either Female, Male, Transgender or Unknown.
- **age (int)**: Age of the patient. Ages lower than 1 y.o. are assigned 0 as age.
- **case_text (string)**: Self-explanatory.
- **keywords (string)**: Keywords are taken from the keywords section that is sometimes available in the content of the article.
- **pics_array (int)**: image
- **Caption (string)**: Image caption.

## Initial Data Collect and Preprocessing
1. The original MultiCaRe Dataset, approximately 9GB in size, encompasses a diverse range of medical specialties including oncology, cardiology, surgery, and pathology. To create your tuberculosis-focused subset, the dataset undergoes a filtering process based on specific criteria:
- Case Report Selection: The selection criterion for case reports is the presence of keywords such as 'tuberculosis' or 'tb'. This ensures that only reports relevant to tuberculosis are included.
- Caption Filtering: The dataset is further refined by filtering captions that contain keywords like 'ct', 'lung', or 'chest'.
- Image Labeling: Finally, the images are chosen based on the presence of labels 'ct' and 'lung'. This dual-label requirement ensures that the selected images are relevant to CT scans of the lungs, which are instrumental in detecting and assessing tuberculosis.
- Through this meticulous filtering process, an initial tuberculosis dataset is compiled from the broader MultiCaRe Dataset. This dataset is messy, contains many diffferent files.
2. To enhance the quality and relevance of the tuberculosis dataset, additional processing steps are implemented after in the Hugging Face python script after the initial filtration from the MultiCaRe Dataset:
- Exclusion of Records with Missing Age Information.
- Merge of data from difference files, including .csv, .JSON, and .jpg.

## Social Impact
The multimodal dataset of tuberculosis patients, meticulously curated from the larger MultiCaRe Dataset, stands to have a significant social impact, particularly in the field of public health and medical research. Tuberculosis (TB) remains a major global health issue, especially in low- and middle-income countries, and the integration of CT imaging with clinical case reports in this dataset provides a rich resource for advanced diagnostic and treatment research. By facilitating the development of more precise algorithms for CT image segmentation and classification, as well as enhancing natural language processing (NLP) techniques for extracting medical terms from clinical notes, this dataset has the potential to improve the accuracy and efficiency of TB diagnosis.

## Limitations

## Citation
**BibTeX:**
```bibtex
@dataset{NievasOffidani2023MultiCaRe,
  author       = {Nievas Offidani, M. and Delrieux, C.},
  title        = {The MultiCaRe Dataset: A Multimodal Case Report Dataset with Clinical Cases, Labeled Images and Captions from Open Access PMC Articles},
  year         = {2023},
  version      = {1.0},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.10079370},
  url          = {https://doi.org/10.5281/zenodo.10079370},
}
```