Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Ana M. Mota"'
Publikováno v:
Journal of Imaging, Vol 10, Iss 9, p 218 (2024)
Breast cancer is the most commonly diagnosed cancer worldwide. The therapy used and its success depend highly on the histology of the tumor. This study aimed to explore the potential of predicting the molecular subtype of breast cancer using radiomic
Externí odkaz:
https://doaj.org/article/9d4a0ab0c76342ae91c6e541501a7fb4
Publikováno v:
Biomedicines, Vol 12, Iss 6, p 1371 (2024)
Breast cancer remains a leading cause of mortality among women, with molecular subtypes significantly influencing prognosis and treatment strategies. Currently, identifying the molecular subtype of cancer requires a biopsy—a specialized, expensive,
Externí odkaz:
https://doaj.org/article/71bf6da697c44266a918f32478d52b69
Publikováno v:
Journal of Imaging, Vol 9, Iss 6, p 119 (2023)
Currently, breast cancer is the most commonly diagnosed type of cancer worldwide. Digital Breast Tomosynthesis (DBT) has been widely accepted as a stand-alone modality to replace Digital Mammography, particularly in denser breasts. However, the image
Externí odkaz:
https://doaj.org/article/4200c0deaf064a7da8ae8ad95ad8bb07
Publikováno v:
Journal of Imaging, Vol 8, Iss 9, p 231 (2022)
Microcalcification clusters (MCs) are among the most important biomarkers for breast cancer, especially in cases of nonpalpable lesions. The vast majority of deep learning studies on digital breast tomosynthesis (DBT) are focused on detecting and cla
Externí odkaz:
https://doaj.org/article/7250aa6db6df49069ba58eed56e5e827
Publikováno v:
Journal of Imaging, Vol 6, Iss 7, p 64 (2020)
3D volume rendering may represent a complementary option in the visualization of Digital Breast Tomosynthesis (DBT) examinations by providing an understanding of the underlying data at once. Rendering parameters directly influence the quality of rend
Externí odkaz:
https://doaj.org/article/6d515bf4988e49ada35d8cf9db2cedde
Publikováno v:
Journal of Imaging; Volume 9; Issue 6; Pages: 119
Currently, breast cancer is the most commonly diagnosed type of cancer worldwide. Digital Breast Tomosynthesis (DBT) has been widely accepted as a stand-alone modality to replace Digital Mammography, particularly in denser breasts. However, the image
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=multidiscipl::a20430493b58529a5e19d310f862d394
Publikováno v:
IEEE transactions on medical imaging. 39(12)
Digital Breast Tomosynthesis (DBT) presents out-of-plane artifacts caused by features of high intensity. Given observed data and knowledge about the point spread function (PSF), deconvolution techniques recover data from a blurred version. However, a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ce4878f105def8cda3e991ab08b6cd76
https://pubmed.ncbi.nlm.nih.gov/32746152
https://pubmed.ncbi.nlm.nih.gov/32746152
Publikováno v:
15th International Workshop on Breast Imaging (IWBI2020).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a0b0ea92565f81bf3d3be0a38dcb9324
https://doi.org/10.1117/12.2559932
https://doi.org/10.1117/12.2559932
Publikováno v:
Computer methods and programs in biomedicine. 195
Background and objective Total Variation (TV) minimization algorithms have achieved great attention due to the virtue of decreasing noise while preserving edges. The purpose of this work is to implement and evaluate two TV minimization methods in 3D.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb56cb80605698421033b0773ded243f
https://pubmed.ncbi.nlm.nih.gov/32480190
https://pubmed.ncbi.nlm.nih.gov/32480190
Publikováno v:
Medical Physics. 42:2827-2836
Purpose: Compressed sensing (CS) is a new approach in medical imaging which allows a sparse image to be reconstructed from undersampled data. Total variation (TV) based minimization algorithms are the one CS technique that has achieved great success
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5d597d6de0324036d711bd44c97163e3
https://doi.org/10.1118/1.4919680
https://doi.org/10.1118/1.4919680