Raju Gudhe - UEFConnect

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Keywords

Artifical intelligence breast cancer deep learning Representation learning

Publications

7/7 items

A deep learning-driven approach for breast cancer risk and outcome prediction
Gudhe, Naga Raju, 2025, Publications of the University of Eastern Finland. Dissertations in Health Sciences. G5 Doctoral dissertation (article)
A Multi-View Deep Evidential Learning Approach for Mammogram Density Classification
Gudhe, Naga Raju; Mazen, Sudah; Sund, Reijo; Kosma, Veli-Matti; Behravan, Hamid; Mannermaa, Arto, 2024, IEEE access, 12, 67889-67909. A1 Journal article (refereed), original research
A dataset of mammography images with area-based breast density values, breast area, and dense tissue segmentation masks
Behravan, Hamid; Gudhe, Naga Raju; Okuma, Hidemi; Sudah, Mazen; Mannermaa, Arto, 2024, Data in brief, 57, 110980. A1 Journal article (refereed), original research
Nuclei instance segmentation from histopathology images using Bayesian dropout based deep learning
Gudhe, Naga Raju; Kosma, Veli-Matti; Behravan, Hamid; Mannermaa, Arto, 2023, Bmc medical imaging, 23, 1, 162. A1 Journal article (refereed), original research
Predicting cell type counts in whole slide histology images using evidential multi-task learning
Gudhe, Naga Raju; Sudah, Mazen; Mannermaa, Arto; Kosma, Veli-Matti; Behravan, Hamid, 2023, Tomaszewski, John E; Ward, Aaron D, Medical Imaging 2023: Digital and Computational Pathology, 239-247. A4 Conference proceedings
Area-based breast percentage density estimation in mammograms using weight-adaptive multitask learning
Gudhe, Naga Raju; Behravan, Hamid; Sudah, Mazen; Okuma, Hidemi; Vanninen, Ritva; Kosma, Veli-Matti; Mannermaa, Arto, 2022, Scientific reports, 12, 1, 12060. A1 Journal article (refereed), original research
Multi-level dilated residual network for biomedical image segmentation
Gudhe, Naga Raju; Behravan, Hamid; Sudah, Mazen; Okuma, Hidemi; Vanninen, Ritva; Kosma, Veli-Matti; Mannermaa, Arto, 2021, Scientific reports, 11, 1, 14105. A1 Journal article (refereed), original research