Update README.md

README.md

@@ -28,20 +28,23 @@ size_categories:
 
 **Breast MRI** plays a pivotal role in breast cancer detection, diagnosis, and treatment planning. However, most existing segmentation models fail to distinguish between the **left and right breasts**, limiting their usefulness in tasks such as **unilateral classification, response evaluation, or post-mastectomy follow-up**.
 
-In this work, we introduce the **first publicly available large-scale dataset with explicit left and right breast segmentation labels**, comprising **over 13,000 3D MRI scans**. Accompanying this dataset is a **robust nnU-Net–based segmentation model**, trained specifically to identify and separate left and right breast regions in clinical MRI data. This resource provides a foundation for developing anatomically aware AI models and enables large-scale pretraining for downstream breast MRI tasks.
+In this work, we introduce the **first publicly available large-scale dataset with explicit left and right breast segmentation labels**, comprising **over 17,000 3D MRI scans**. Accompanying this dataset is a **robust nnU-Net–based segmentation model**, trained specifically to identify and separate left and right breast regions in clinical MRI data. This resource provides a foundation for developing anatomically aware AI models and enables large-scale pretraining for downstream breast MRI tasks.
 
 ---
 
 ## 📂 Dataset
 
-This repository includes a complete **13k+ MRI scan dataset** with left/right segmentation masks. We curated a diverse and comprehensive breast MRI dataset by aggregating scans from multiple publicly available sources:
+This repository includes a complete **17k+ MRI scan dataset** with left/right segmentation masks, 3000 lesion classification targets and 467 lesion segmentation masks. We curated a diverse and comprehensive breast MRI dataset by aggregating scans from multiple publicly available sources:
 
-- [Duke-Breast-Cancer-MRI dataset](https://www.cancerimagingarchive.net/collection/duke-breast-cancer-mri/)
-- [MAMA-MIA](https://github.com/LidiaGarrucho/MAMA-MIA)
-- [Advanced-MRI-Breast-Lesions](https://www.cancerimagingarchive.net/collection/advanced-mri-breast-lesions/)
-- [EA1141](https://www.cancerimagingarchive.net/collection/ea1141/)
+- Duke-Breast-Cancer-MRI dataset
+- MAMA-MIA
+- Advanced-MRI-Breast-Lesions
+- EA1141
+- ODELIA
+- ISPY1
+- ISPY2
 
-In total, the dataset comprises **13,752 3D scans**. It includes a variety of common MRI modalities such as:
+In total, the dataset comprises **17,956 3D scans**. It includes a variety of common MRI modalities such as:
 
 - T1-weighted (T1)
 - T1 with contrast (T1+C)
@@ -58,6 +61,8 @@ To ensure consistency and quality, we only included MRI volumes with:
 - At least **32 slices per axis**
 - A resolution of **≤ 3×3×3 mm**
 
+Additionally we provide classification labels for 
+
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 ## 📄 Citation
@@ -71,4 +76,27 @@ If you use this dataset or model in your work, please cite:
   journal   = {arXiv preprint arXiv:2507.13830},
   year      = {2025}
 }
-```
+```
+
+The Breast Divider dataset includes public DCE-MRI images from several different collections under the following licenses:
+
+#### EA1141 (License [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)) 
+> Comstock, C. E., Gatsonis, C., Newstead, G. M., Snyder, B. S., Gareen, I. F., Bergin, J. T., Rahbar, H., Sung, J. S., Jacobs, C., Harvey, J. A., Nicholson, M. H., Ward, R. C., Holt, J., Prather, A., Miller, K. D., Schnall, M. D., & Kuhl, C. K. (2023). Abbreviated Breast MRI and Digital Tomosynthesis Mammography in Screening Women With Dense Breasts (EA1141) (Version 1) [Data set]. The Cancer Imaging Archive. [https://doi.org/10.7937/2BAS-HR33](https://doi.org/10.7937/2BAS-HR33)
+
+#### AMBL (License [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)) 
+> Daniels, D., Last, D., Cohen, K., Mardor, Y., & Sklair-Levy, M. (2024). Standard and Delayed Contrast‑Enhanced MRI of Malignant and Benign Breast Lesions with Histological and Clinical Supporting Data (AMBL) (Version 2) [Data set]. The Cancer Imaging Archive. [https://doi.org/10.7937/C7X1-YN57](https://doi.org/10.7937/C7X1-YN57)
+
+#### ISPY1 Trial (License [CC BY 3.0](https://creativecommons.org/licenses/by/3.0/)) 
+> David Newitt, Nola Hylton, on behalf of the I-SPY 1 Network and ACRIN 6657 Trial Team. (2016). Multi-center breast DCE-MRI data and segmentations from patients in the I-SPY 1/ACRIN 6657 trials. The Cancer Imaging Archive. [https://doi.org/10.7937/K9/TCIA.2016.HdHpgJLK](https://www.cancerimagingarchive.net/collection/ispy1/)
+
+#### ISPY2 Trial (License [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) 
+> Li, W., Newitt, D. C., Gibbs, J., Wilmes, L. J., Jones, E. F., Arasu, V. A., Strand, F., Onishi, N., Nguyen, A. A.-T., Kornak, J., Joe, B. N., Price, E. R., Ojeda-Fournier, H., Eghtedari, M., Zamora, K. W., Woodard, S. A., Umphrey, H., Bernreuter, W., Nelson, M., … Hylton, N. M. (2022). I-SPY 2 Breast Dynamic Contrast Enhanced MRI Trial (ISPY2)  (Version 1) [Data set]. The Cancer Imaging Archive. [https://doi.org/10.7937/TCIA.D8Z0-9T85](https://doi.org/10.7937/TCIA.D8Z0-9T85)
+
+#### Duke-Breast-Cancer-MRI (License [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/))
+> Saha, A., Harowicz, M. R., Grimm, L. J., Weng, J., Cain, E. H., Kim, C. E., Ghate, S. V., Walsh, R., & Mazurowski, M. A. (2021). Dynamic contrast-enhanced magnetic resonance images of breast cancer patients with tumor locations [Data set]. The Cancer Imaging Archive. [https://doi.org/10.7937/TCIA.e3sv-re93](https://doi.org/10.7937/TCIA.e3sv-re93)
+
+#### MAMA-MIA (License [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/))
+> Garrucho, L., Kushibar, K., Reidel, C.-A., Joshi, S., Osuala, R., Tsirikoglou, A., Bobowicz, M., del Riego, J., Catanese, A., Gwoździewicz, K., Cosaka, M.-L., Abo-Elhoda, P. M., Tantawy, S. W., Sakrana, S. S., Shawky-Abdelfatah, N. O., Salem, A. M. A., Kozana, A., Divjak, E., Ivanac, G., Nikiforaki, K., Klontzas, M. E., García-Dosdá, R., Gulsun-Akpinar, M., Lafcı, O., Mann, R., Martín-Isla, C., Prior, F., Marias, K., Starmans, M. P. A., Strand, F., Díaz, O., Igual, L., & Lekadir, K. (2025). A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations. [https://doi.org/10.1038/s41597-025-04707-4](https://doi.org/10.1038/s41597-025-04707-4)
+
+#### ODELIA (License [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/))
+> Müller-Franzes, G., Escudero Sánchez, L., Payne, N., Athanasiou, A., Kalogeropoulos, M., Lopez, A., Soro Busto, A. M., Camps Herrero, J., Rasoolzadeh, N., Zhang, T., Mann, R., Jutz, D., Bode, M., Kuhl, C., Veldhuis, W., Saldanha, O. L., Zhu, J., Kather, J. N., Truhn, D., & Gilbert, F. J., on behalf of the ODELIA Consortium. (2025). A European Multi-Center Breast Cancer MRI Dataset. [https://doi.org/10.48550/arXiv.2506.00474](https://doi.org/10.48550/arXiv.2506.00474)