Number of Modalities Across the Dataset
|
Patients |
CT |
MRI |
WSI |
Genomics |
Clinical |
Train |
497 |
189 |
35 |
497 |
361 |
497 |
Test |
121 |
59 |
13 |
121 |
101 |
121 |
Total |
618 |
248 |
48 |
618 |
462 |
618 |
Patients' Clinical and Genomic Data
Here you can access the metadata with the train/test split (from CPTAC and TCGA repositories).
Download the Multi-Modal Data
Download WSIs
Below you have the links where you can access the original dataset both from TCGA (XXGB) and CPTAC ('Tissue Slide Images': with 190GB) and the CSV containing the selected files that we used. Be aware that for the TCGA we provide the manifest file that you can use directly with the GDC Data Transfer tool.
Download the CT and MRI images
Below you can access the CT and MRI images from the same patients both from TCGA ('Images': 91.56GB) and CPTAC repositories ('Radiology Images': 56.58GB), as well as the CSV with the filenames and IDs that we used in our repository.
We discarded the localization scans, the pre-contrast ones, and scans that were acquired with a significant time lapse from the diagnosis (years). We also removed the scans from the coronal and sagittal views, to minimize
domain shifts. We reduced the number of volumes to 736 for CT and 552 for MRI.
Use Our Features
If you don't want to download the original images, you can use our features.
Access the CT/MRI/WSI Features
Here you can access the different folders containing the multi-modal features:
Per Patient Files (Selected Using MIL)
Access the CT/MRI/WSI Features
Several patients presented more than one CT/MRI scan and WSI image. We opted to reduce the amount of data used by our multi-modal system to a single CT, MRI, and WSI per patient.
We implemented a novel patient-level MIL framework that automatically selects the best imaging modalities from the available pool.
Here you can access the different CSV files containing the selected files per patient that were used in our paper:
Scientific Paper
If you want more information about this dataset feel free to read our paper:
Tiago Mota, Maria Rita Fonseca Verdelho, Diogo José Pereira Araújo, Alceu Bissoto, Carlos Santiago, Catarina Barata,
MMIST-ccRCC: A Real-World Medical Dataset for the Development of Multi-Modal Systems, Data Curation and Augmentation in Enhancing Medical Imaging Applications Workshop (archival) @CVPR 2024.