IMP Whole-Slide Images of Colorectal Samples 2024

The IMP-CRS 2024 dataset contains 5333 colorectal biopsy and polypectomy slides, retrieved from the data archive of IMP Diagnostics laboratory, Portugal, digitised at 40X by 2 Leica GT450 WSI scanners.

All cases are classified within one of the three categories:

  • Non-neoplastic (label 0)

  • Low-grade lesions (label 1) - conventional adenomas with low-grade dysplasia

  • High-grade lesions (label 2) - conventional adenomas with high-grade dysplasia, intra-mucosal carcinomas and invasive adenocarcinomas.

Please read the Download and Usage information document below

Access to the dataset: https://open-datasets.inesctec.pt/NQ3sxFMZ/

Data and Resources

Additional Info

Field Value
Author Pedro C. Neto, Diana Montezuma, Sara P. Oliveira, Domingos Oliveira, João Fraga, Ana Monteiro, Liliana Ribeiro, Sofia Gonçalves, Stefan Reinhard, Inti Zlobec, Isabel M. Pinho, Jaime S. Cardoso
Last Updated January 23, 2024, 12:24 (UTC)
Created January 3, 2024, 16:37 (UTC)
Citation Neto, P. C., Montezuma, D., P. Oliveira, S., Oliveira, D., Fraga, J., Monteiro, A., Ribeiro, L., Gonçalves, S., Reinhard, S., Zlobec, I., Pinto, I. M., & Cardoso, J. S. (2024). IMP Whole-Slide Images of Colorectal Samples 2024 [Data set]. INESC TEC. https://doi.org/10.25747/FB1Q-J507
Creation Date 2024
DOI https://doi.org/10.25747/fb1q-j507
File Size 5333 whole silde images (~5 TB)
Format .svs
Instrument Name Leica GT450 WS
Project cadpath.ai
Relation [1] Oliveira, S.P., Neto, P.C., Fraga, J., Montezuma, D., Monteiro, A., Monteiro, J., Ribeiro, L., Gonçalves, S., Pinto, I.M. and Cardoso, J.S., 2021. CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance. Scientific Reports, 11(1), pp.1-15. https://doi.org/10.1038/s41598-021-93746-z
Relation [2] Neto, P.C., Oliveira, S.P., Montezuma, D., Fraga, J., Monteiro, A., Ribeiro, L., Gonçalves, S., Pinto, I.M. and Cardoso, J.S., 2022. iMIL4PATH: A semi-supervised interpretable approach for colorectal whole-slide images. Cancers, 14(10). https://doi.org/10.3390/cancers14102489
Relation [3] Neto, P.C., Montezuma, D., Oliveira, S.P., Oliveira, D., Fraga, J., Monteiro, A., Monteiro, J., Ribeiro, L., Gonçalves, S., Reinhard, S., Zlobec ,I. , Pinto, I.M. and Cardoso, J.S., 2024. An interpretable machine learning system for colorectal cancer diagnosis from pathology slides. npj Precision Oncology. *DOI TBD*
Software OpenSlide or any other digital pathology software
Type of Instrument Digital Pathology Scanner