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/

데이터와 리소스

추가 정보

필드 값
저자 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
최종 업데이트 4월 29, 2024, 13:17 (UTC)
생성됨 1월 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
파일 크기 5333 whole silde images (~5 TB)
포맷 .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. https://doi.org/10.1038/s41698-024-00539-4
Software OpenSlide or any other digital pathology software
Type of Instrument Digital Pathology Scanner