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Behravan H, Gudhe NR, Okuma H, Sudah M, Mannermaa A. A dataset of mammography images with area-based breast density values, breast area, and dense tissue segmentation masks. Data in Brief. 2024 Dec 1;57:110980.
Gudhe NR, Mazen S, Sund R, Kosma VM, Behravan H, Mannermaa A. A multi-view deep evidential learning approach for mammogram density classification. IEEE Access. 2024 May 9.
Gudhe, N.R., Kosma, VM., Behravan, H. et al. Nuclei instance segmentation from histopathology images using Bayesian dropout based deep learning. BMC Med Imaging 23, 162 (2023). https://doi.org/10.1186/s12880-023-01121-3
R. Gudhe, H. Behravan, M. Sudah, V.-M Kosmaa, and A. Mannermaa. “Predicting cell type counts in whole slide histology images using evidential multi-task learning”. SPIE Medical Imaging, (2023).
Gudhe, N.R., Behravan, H., Sudah, M. et al. Area-based breast percentage density estimation in mammograms using weight-adaptive multitask learning. Sci Rep 12, 12060 (2022). https://doi.org/10.1038/s41598-022-16141-2
Gudhe, N.R., Behravan, H., Sudah, M. et al. Multi-level dilated residual network for biomedical image segmentation. Sci Rep 11, 14105 (2021). https://doi.org/10.1038/s41598-021-93169-w
Behravan, H., Hartikainen, J.M., Tengström, M. et al. Predicting breast cancer risk using interacting genetic and demographic factors and machine learning. Sci Rep 10, 11044 (2020). https://doi.org/10.1038/s41598-020-66907-9
Behravan, H., Hartikainen, J.M., Tengström, M. et al. Machine learning identifies interacting genetic variants contributing to breast cancer risk: A case study in Finnish cases and controls. Sci Rep 8, 13149 (2018). https://doi.org/10.1038/s41598-018-31573-5
If you are interested in a project related to cancer risk and patient outcome prediction, you can consider reaching out to us.
University of Eastern Finland, Institute of Clinical Medicine
Yliopistonranta 1 C, Canthia building, Kuopio, Finland
hamid.behravan[@]uef.fi
+358 50 5139836
Copyright 2023 – Hamid Behravan
