Zobrazeno 1 - 10 of 20 pro vyhledávání: '"Samira Masoudi"'
1
Akademický článek
Feature Interpretation Using Generative Adversarial Networks (FIGAN): A Framework for Visualizing a CNN’s Learned Features
Autor: Kyle A. Hasenstab, Justin Huynh, Samira Masoudi, Guilherme M. Cunha, Michael Pazzani, Albert Hsiao
Publikováno v: IEEE Access, Vol 11, Pp 5144-5160 (2023)
Convolutional neural networks (CNNs) are increasingly being explored and used for a variety of classification tasks in medical imaging, but current methods for post hoc explainability are limited. Most commonly used methods highlight portions of the
Externí odkaz: https://doaj.org/article/9ee2e806bfbd4c7e94a74b38f9293e5e
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2
Akademický článek
Deep Learning Radiographic Assessment of Pulmonary Edema: Optimizing Clinical Performance, Training With Serum Biomarkers
Autor: Justin Huynh, Samira Masoudi, Abraham Noorbakhsh, Amin Mahmoodi, Seth Kligerman, Andrew Yen, Kathleen Jacobs, Lewis Hahn, Kyle Hasenstab, Michael Pazzani, Albert Hsiao
Publikováno v: IEEE Access, Vol 10, Pp 48577-48588 (2022)
A major obstacle when developing convolutional neural networks (CNNs) for medical imaging is the acquisition of training labels: Most current approaches rely on manual class labels from physicians, which may be challenging to obtain. Clinical biomark
Externí odkaz: https://doaj.org/article/2e89694ec11240348c75a0f7e853a24b
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3
Akademický článek
Deep Learning Based Staging of Bone Lesions From Computed Tomography Scans
Autor: Samira Masoudi, Sherif Mehralivand, Stephanie A. Harmon, Nathan Lay, Liza Lindenberg, Esther Mena, Peter A. Pinto, Deborah E. Citrin, James L. Gulley, Bradford J. Wood, William L. Dahut, Ravi A. Madan, Ulas Bagci, Peter L. Choyke, Baris Turkbey
Publikováno v: IEEE Access, Vol 9, Pp 87531-87542 (2021)
In this study, we formulated an efficient deep learning-based classification strategy for characterizing metastatic bone lesions using computed tomography scans (CTs) of prostate cancer patients. For this purpose, 2,880 annotated bone lesions from CT
Externí odkaz: https://doaj.org/article/453e9898b0f745f984167e9d0caf9d9f
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4
A Cascaded Deep Learning–Based Artificial Intelligence Algorithm for Automated Lesion Detection and Classification on Biparametric Prostate Magnetic Resonance Imaging
Autor: Peter A. Pinto, Stephanie Harmon, Samira Masoudi, Dong Yang, Holger R. Roth, Deepak Kesani, Baris Turkbey, Daguang Xu, Thomas Sanford, Sherif Mehralivand, Bradford J. Wood, Maria J. Merino, Nathan Lay, Peter L. Choyke, Ziyue Xu
Publikováno v: Academic Radiology. 29:1159-1168
Rationale and objectives Prostate MRI improves detection of clinically significant prostate cancer; however, its diagnostic performance has wide variation. Artificial intelligence (AI) has the potential to assist radiologists in the detection and cla
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fe92ae17e43082bee94870a20e48a99d
https://doi.org/10.1016/j.acra.2021.08.019
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5
Optimizing Micropropagation of Apple (Malus × Domestica Borkh) and in Vitro Root Induction By Piriformospora indica
Autor: Hassan Hajnajari, Amin Alidadi, Maryam Jafarkhani Kermani, Zahra Saadat Hosseini, Ali Soleimani, Samira Masoudi
Publikováno v: Agriculture, Vol 66, Iss 4, Pp 137-147 (2020)
The inoculation of plant species with mycorrhiza fungus Piriformospora indica results in enhancement of growth, increase in yield, and induction of resistance to biotic and abiotic diseases through improvement of the root system. The aim of the prese
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7220d6ad45b30c2fd90c72f32deeedd5
https://doi.org/10.2478/agri-2020-0013
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7
Deep Learning Based Staging of Bone Lesions From Computed Tomography Scans
Autor: Peter L. Choyke, Deborah Citrin, Baris Turkbey, Sherif Mehralivand, Liza Lindenberg, James L. Gulley, Ravi A. Madan, William L. Dahut, Esther Mena, Nathan Lay, Bradford J. Wood, Samira Masoudi, Stephanie Harmon, Ulas Bagci, Peter A. Pinto
Publikováno v: IEEE access : practical innovations, open solutions
IEEE Access, Vol 9, Pp 87531-87542 (2021)
In this study, we formulated an efficient deep learning-based classification strategy for characterizing metastatic bone lesions using computed tomography scans (CTs) of prostate cancer patients. For this purpose, 2,880 annotated bone lesions from CT
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4b49cbb3121687f7d4ffac8949906d09
https://pubmed.ncbi.nlm.nih.gov/34733603
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8
Deep learning-based artificial intelligence for prostate cancer detection at biparametric MRI
Autor: Sherif Mehralivand, Dong Yang, Stephanie A. Harmon, Daguang Xu, Ziyue Xu, Holger Roth, Samira Masoudi, Deepak Kesani, Nathan Lay, Maria J. Merino, Bradford J. Wood, Peter A. Pinto, Peter L. Choyke, Baris Turkbey
Publikováno v: Abdominal radiology (New York). 47(4)
To present fully automated DL-based prostate cancer detection system for prostate MRI.MRI scans from two institutions, were used for algorithm training, validation, testing. MRI-visible lesions were contoured by an experienced radiologist. All lesion
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ab2381777258ec0c120d32de3d16d698
https://pubmed.ncbi.nlm.nih.gov/35099572
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