Summary of A Large-scale Multicenter Breast Cancer Dce-mri Benchmark Dataset with Expert Segmentations, by Lidia Garrucho et al.
A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations
by Lidia Garrucho, Kaisar Kushibar, Claire-Anne Reidel, Smriti Joshi, Richard Osuala, Apostolia Tsirikoglou, Maciej Bobowicz, Javier del Riego, Alessandro Catanese, Katarzyna Gwoździewicz, Maria-Laura Cosaka, Pasant M. Abo-Elhoda, Sara W. Tantawy, Shorouq S. Sakrana, Norhan O. Shawky-Abdelfatah, Amr Muhammad Abdo-Salem, Androniki Kozana, Eugen Divjak, Gordana Ivanac, Katerina Nikiforaki, Michail E. Klontzas, Rosa García-Dosdá, Meltem Gulsun-Akpinar, Oğuz Lafcı, Ritse Mann, Carlos Martín-Isla, Fred Prior, Kostas Marias, Martijn P.A. Starmans, Fredrik Strand, Oliver Díaz, Laura Igual, Karim Lekadir
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel multicenter dataset for breast cancer Magnetic Resonance Imaging (MRI) is presented, comprising 1506 pre-treatment T1-weighted dynamic contrast-enhanced MRI cases with expert annotations of primary tumors and non-mass-enhanced regions. The dataset integrates imaging data from four collections in The Cancer Imaging Archive (TCIA), where only 163 cases with expert segmentations were initially available. A deep learning model was trained to produce preliminary segmentations for the remaining cases, which were then corrected and verified by 16 breast cancer experts. The dataset also includes 49 harmonized clinical and demographic variables, as well as pre-trained weights for a baseline nnU-Net model trained on the annotated data. This resource addresses a critical gap in publicly available breast cancer datasets, enabling the development, validation, and benchmarking of advanced deep learning models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new dataset is created to help doctors better diagnose and treat breast cancer using MRI scans. The dataset includes 1500+ MRI cases with expert markings that show where tumors are located. This helps train computers to identify tumors more accurately. The data comes from four different sources, including only 163 cases that were previously labeled by experts. A computer model was used to help label the remaining cases, which were then checked and corrected by breast cancer specialists. This dataset can be used to develop and test new AI models for diagnosing and treating breast cancer. |
Keywords
* Artificial intelligence * Deep learning
