metadata

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
pretty_name: TCGA

Dataset Card for The Cancer Genome Atlas (TCGA) Multimodal 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

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

@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