Datasets:
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---
configs:
- config_name: clinical
data_files:
- split: train
path: Clinical Data (gatortron-base)/*
- config_name: pathology_report
data_files:
- split: train
path: Pathology Report (gatortron-base)/*
- config_name: wsi
data_files:
- split: train
path: Slide Image (UNI)/*
- config_name: molecular
data_files:
- split: train
path: Molecular (SeNMo)/*
language:
- en
tags:
- medical
- multimodal
- tcga
pretty_name: TCGA
license: cc-by-nc-nd-4.0
---
# Dataset Card for The Cancer Genome Atlas (TCGA) Multimodal Dataset
<!-- Provide a quick summary of the dataset. -->
The Cancer Genome Atlas (TCGA) Multimodal Dataset is a comprehensive collection of clinical data, pathology reports, and slide images for cancer patients.
This dataset aims to facilitate research in multimodal machine learning for oncology by providing embeddings generated using state-of-the-art models such as GatorTron and UNI.
- **Curated by:** Lab Rasool
- **Language(s) (NLP):** English
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
```python
from datasets import load_dataset
clinical_dataset = load_dataset("Lab-Rasool/TCGA", "clinical", split="train")
pathology_report_dataset = load_dataset("Lab-Rasool/TCGA", "pathology_report", split="train")
wsi_dataset = load_dataset("Lab-Rasool/TCGA", "wsi", split="train")
molecular_dataset = load_dataset("Lab-Rasool/TCGA", "molecular", split="train")
```
## Dataset Creation
#### Data Collection and Processing
The raw data for this dataset was acquired using MINDS, a multimodal data aggregation tool developed by Lab Rasool.
The collected data includes clinical information, pathology reports, and whole slide images from The Cancer Genome Atlas (TCGA).
The embeddings were generated using the HoneyBee embedding processing tool, which utilizes foundational models such as GatorTron and UNI.
#### Who are the source data producers?
The source data for this dataset was originally collected and maintained by The Cancer Genome Atlas (TCGA) program, a landmark cancer genomics project jointly managed by the National Cancer Institute (NCI).
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
```
@article{honeybee,
title={HoneyBee: A Scalable Modular Framework for Creating Multimodal Oncology Datasets with Foundational Embedding Models},
author={Aakash Tripathi and Asim Waqas and Yasin Yilmaz and Ghulam Rasool},
year={2024},
eprint={2405.07460},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
@article{waqas2024senmo,
title={SeNMo: A self-normalizing deep learning model for enhanced multi-omics data analysis in oncology},
author={Waqas, Asim and Tripathi, Aakash and Ahmed, Sabeen and Mukund, Ashwin and Farooq, Hamza and Schabath, Matthew B and Stewart, Paul and Naeini, Mia and Rasool, Ghulam},
journal={arXiv preprint arXiv:2405.08226},
year={2024}
}
```
### For more information about the data acquisition and processing tools used in creating this dataset, please refer to the following resources:
- MINDS paper: https://pubmed.ncbi.nlm.nih.gov/38475170/
- MINDS codebase: https://github.com/lab-rasool/MINDS
- HoneyBee repository: https://github.com/lab-rasool/HoneyBee/tree/main |