Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Karol Borkowski"'
Autor:
Sylwia Nowakowska, Karol Borkowski, Carlotta M. Ruppert, Anna Landsmann, Magda Marcon, Nicole Berger, Andreas Boss, Alexander Ciritsis, Cristina Rossi
Publikováno v:
Insights into Imaging, Vol 14, Iss 1, Pp 1-11 (2023)
Abstract Objectives Development of automated segmentation models enabling standardized volumetric quantification of fibroglandular tissue (FGT) from native volumes and background parenchymal enhancement (BPE) from subtraction volumes of dynamic contr
Externí odkaz:
https://doaj.org/article/c02b6f553ba844d3bc6936684b713aaf
Autor:
Tician Schnitzler, Carlotta Ruppert, Patryk Hejduk, Karol Borkowski, Jonas Kajüter, Cristina Rossi, Alexander Ciritsis, Anna Landsmann, Hasan Zaytoun, Andreas Boss, Sebastian Schindera, Felice Burn
Publikováno v:
Journal of Imaging, Vol 10, Iss 6, p 147 (2024)
Background: After breast conserving surgery (BCS), surgical clips indicate the tumor bed and, thereby, the most probable area for tumor relapse. The aim of this study was to investigate whether a U-Net-based deep convolutional neural network (dCNN) m
Externí odkaz:
https://doaj.org/article/507906cb142547949a2ccc377d6c548d
Autor:
Patryk Hejduk, Raphael Sexauer, Carlotta Ruppert, Karol Borkowski, Jan Unkelbach, Noemi Schmidt
Publikováno v:
Insights into Imaging, Vol 14, Iss 1, Pp 1-9 (2023)
Abstract Objectives The aim of this study was to develop and validate a commercially available AI platform for the automatic determination of image quality in mammography and tomosynthesis considering a standardized set of features. Materials and met
Externí odkaz:
https://doaj.org/article/d461c4415ac040f0a53c8c7392b7760b
Autor:
Sylwia Nowakowska, Karol Borkowski, Carlotta Ruppert, Patryk Hejduk, Alexander Ciritsis, Anna Landsmann, Magda Marcon, Nicole Berger, Andreas Boss, Cristina Rossi
Publikováno v:
Bioengineering, Vol 11, Iss 6, p 556 (2024)
In DCE-MRI, the degree of contrast uptake in normal fibroglandular tissue, i.e., background parenchymal enhancement (BPE), is a crucial biomarker linked to breast cancer risk and treatment outcome. In accordance with the Breast Imaging Reporting & Da
Externí odkaz:
https://doaj.org/article/cfa70e593357479b951b2056c4790433
Autor:
Anna Landsmann, Carlotta Ruppert, Jann Wieler, Patryk Hejduk, Alexander Ciritsis, Karol Borkowski, Moritz C. Wurnig, Cristina Rossi, Andreas Boss
Publikováno v:
European Radiology Experimental, Vol 6, Iss 1, Pp 1-13 (2022)
Abstract Background We investigated whether features derived from texture analysis (TA) can distinguish breast density (BD) in spiral photon-counting breast computed tomography (PC-BCT). Methods In this retrospective single-centre study, we analysed
Externí odkaz:
https://doaj.org/article/441fc6e307604b019c319366d89857a2
Autor:
Albin Sabani, Anna Landsmann, Patryk Hejduk, Cynthia Schmidt, Magda Marcon, Karol Borkowski, Cristina Rossi, Alexander Ciritsis, Andreas Boss
Publikováno v:
Diagnostics, Vol 12, Iss 7, p 1564 (2022)
The aim of this study was to investigate the potential of a machine learning algorithm to classify breast cancer solely by the presence of soft tissue opacities in mammograms, independent of other morphological features, using a deep convolutional ne
Externí odkaz:
https://doaj.org/article/ba852106417c4c48943257fe07b9309c
Autor:
Frederik Abel, Anna Landsmann, Patryk Hejduk, Carlotta Ruppert, Karol Borkowski, Alexander Ciritsis, Cristina Rossi, Andreas Boss
Publikováno v:
Diagnostics, Vol 12, Iss 6, p 1347 (2022)
The purpose of this study was to determine the feasibility of a deep convolutional neural network (dCNN) to accurately detect abnormal axillary lymph nodes on mammograms. In this retrospective study, 107 mammographic images in mediolateral oblique pr
Externí odkaz:
https://doaj.org/article/a0013a6e8ac341f3b468d65470ac3d48
Autor:
Anna Landsmann, Jann Wieler, Patryk Hejduk, Alexander Ciritsis, Karol Borkowski, Cristina Rossi, Andreas Boss
Publikováno v:
Diagnostics, Vol 12, Iss 1, p 181 (2022)
The aim of this study was to investigate the potential of a machine learning algorithm to accurately classify parenchymal density in spiral breast-CT (BCT), using a deep convolutional neural network (dCNN). In this retrospectively designed study, 634
Externí odkaz:
https://doaj.org/article/7011cd74c309451197e8597952e5f470
Autor:
Karol Borkowski, Artur Krzyżak
Publikováno v:
Annals of computer science and information systems, Vol 8, Pp 935-938 (2016)
Externí odkaz:
https://doaj.org/article/1d12305bb809490488b773c39fd8db73
Autor:
Raphael Sexauer, Patryk Hejduk, Karol Borkowski, Carlotta Ruppert, Thomas Weikert, Sophie Dellas, Noemi Schmidt
Publikováno v:
European Radiology.
Objectives High breast density is a well-known risk factor for breast cancer. This study aimed to develop and adapt two (MLO, CC) deep convolutional neural networks (DCNN) for automatic breast density classification on synthetic 2D tomosynthesis reco
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cd000bf1817988269040709e6f28033b
https://doi.org/10.1007/s00330-023-09474-7
https://doi.org/10.1007/s00330-023-09474-7