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Dooman Arefan

Showing results (1-10 of 34) with videos related to

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Journal of Digital Imaging|June 5, 2020
Pre-operative Microvascular Invasion Prediction Using Multi-parametric Liver MRI RadiomicsGiacomo Nebbia, Qian Zhang, Dooman Arefan, et al.
Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence|April 10, 2026
<math><mi>Δ</mi> <mi>t</mi></math> -Mamba3D: A Time-Aware Spatio-Temporal State-Space Model for Breast Cancer Risk PredictionZhengbo Zhou, Dooman Arefan, Margarita Zuley, et al.
Medical Physics|October 15, 2020
Machine learning prediction of axillary lymph node metastasis in breast cancer: 2D versus 3D radiomic featuresDooman Arefan, Ruimei Chai, Min Sun, et al.
Journal of Cancer Research and Therapeutics|March 9, 2018
Calculation of the contrast of the calcification in digital mammography system: Gate validationDooman Arefan, Alireza Talebpour, Nasrin Ahmadinejhad, et al.
Proceedings. IEEE International Symposium on Biomedical Imaging|October 27, 2023
Human Not in the Loop: Objective Sample Difficulty Measures for Curriculum LearningZhengbo Zhou, Jun Luo, Dooman Arefan, et al.
Resuscitation|July 6, 2023
Interpretable machine learning model for imaging-based outcome prediction after cardiac arrestChang Liu, Jonathan Elmer, Dooman Arefan, et al.
BMC Medical Imaging|January 31, 2022
A CT-based radiomics model for predicting renal capsule invasion in renal cell carcinomaLu Yang, Long Gao, Dooman Arefan, et al.
Breast Cancer Research : BCR|May 24, 2024
Deep learning of mammogram images to reduce unnecessary breast biopsies: a preliminary studyChang Liu, Min Sun, Dooman Arefan, et al.
Academic Radiology|January 11, 2025
A Radiomic-Clinical Model of Contrast-Enhanced Mammography for Breast Cancer Biopsy Outcome PredictionChang Liu, Priya Patel, Dooman Arefan, et al.
Machine Learning in Medical Imaging. MLMI (Workshop)|October 9, 2023
Knowledge-Guided Multiview Deep Curriculum Learning for Elbow Fracture ClassificationJun Luo, Gene Kitamura, Dooman Arefan, et al.
Pageof 4

Showing results (1-10 of 34) with videos related to

Sort By:
Pageof 4
Journal of Digital Imaging|June 5, 2020
Pre-operative Microvascular Invasion Prediction Using Multi-parametric Liver MRI RadiomicsGiacomo Nebbia, Qian Zhang, Dooman Arefan, et al.
Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence|April 10, 2026
<math><mi>Δ</mi> <mi>t</mi></math> -Mamba3D: A Time-Aware Spatio-Temporal State-Space Model for Breast Cancer Risk PredictionZhengbo Zhou, Dooman Arefan, Margarita Zuley, et al.
Medical Physics|October 15, 2020
Machine learning prediction of axillary lymph node metastasis in breast cancer: 2D versus 3D radiomic featuresDooman Arefan, Ruimei Chai, Min Sun, et al.
Journal of Cancer Research and Therapeutics|March 9, 2018
Calculation of the contrast of the calcification in digital mammography system: Gate validationDooman Arefan, Alireza Talebpour, Nasrin Ahmadinejhad, et al.
Proceedings. IEEE International Symposium on Biomedical Imaging|October 27, 2023
Human Not in the Loop: Objective Sample Difficulty Measures for Curriculum LearningZhengbo Zhou, Jun Luo, Dooman Arefan, et al.
Resuscitation|July 6, 2023
Interpretable machine learning model for imaging-based outcome prediction after cardiac arrestChang Liu, Jonathan Elmer, Dooman Arefan, et al.
BMC Medical Imaging|January 31, 2022
A CT-based radiomics model for predicting renal capsule invasion in renal cell carcinomaLu Yang, Long Gao, Dooman Arefan, et al.
Breast Cancer Research : BCR|May 24, 2024
Deep learning of mammogram images to reduce unnecessary breast biopsies: a preliminary studyChang Liu, Min Sun, Dooman Arefan, et al.
Academic Radiology|January 11, 2025
A Radiomic-Clinical Model of Contrast-Enhanced Mammography for Breast Cancer Biopsy Outcome PredictionChang Liu, Priya Patel, Dooman Arefan, et al.
Machine Learning in Medical Imaging. MLMI (Workshop)|October 9, 2023
Knowledge-Guided Multiview Deep Curriculum Learning for Elbow Fracture ClassificationJun Luo, Gene Kitamura, Dooman Arefan, et al.
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