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Patryk Hejduk

Showing results (1-10 of 14) with videos related to

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Insights Into Imaging|May 18, 2023
Automatic and standardized quality assurance of digital mammography and tomosynthesis with deep convolutional neural networksPatryk Hejduk, Raphael Sexauer, Carlotta Ruppert, et al.
Investigative Radiology|October 10, 2020
Classification of Mammographic Breast Microcalcifications Using a Deep Convolutional Neural Network: A BI-RADS-Based ApproachClaudio Schönenberger, Patryk Hejduk, Alexander Ciritsis, et al.
European Radiology|February 11, 2022
Fully automatic classification of automated breast ultrasound (ABUS) imaging according to BI-RADS using a deep convolutional neural networkPatryk Hejduk, Magda Marcon, Jan Unkelbach, et al.
Diagnostics (Basel, Switzerland)|January 21, 2022
Applied Machine Learning in Spiral Breast-CT: Can We Train a Deep Convolutional Neural Network for Automatic, Standardized and Observer Independent Classification of Breast Density?Anna Landsmann, Jann Wieler, Patryk Hejduk, et al.
Heliyon|August 13, 2021
Deep learning for the standardized classification of Ki-67 in vulva carcinoma: A feasibility studyMatthias Choschzick, Mariam Alyahiaoui, Alexander Ciritsis, et al.
European Radiology|March 1, 2023
Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networksRaphael Sexauer, Patryk Hejduk, Karol Borkowski, et al.
Clinical Imaging|January 5, 2023
Detection of microcalcifications in photon-counting dedicated breast-CT using a deep convolutional neural network: Proof of principleAnna Landsmann, Carlotta Ruppert, Karol Borkowski, et al.
Diagnostics (Basel, Switzerland)|June 24, 2022
Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural NetworkFrederik Abel, Anna Landsmann, Patryk Hejduk, et al.
Medicine|July 25, 2020
Fully automatic classification of breast MRI background parenchymal enhancement using a transfer learning approachKarol Borkowski, Cristina Rossi, Alexander Ciritsis, et al.
Plos One|June 20, 2015
Hydrogel Nanofilaments via Core-Shell ElectrospinningPaweł Nakielski, Sylwia Pawłowska, Filippo Pierini, et al.
Pageof 2

Showing results (1-10 of 14) with videos related to

Sort By:
Pageof 2
Insights Into Imaging|May 18, 2023
Automatic and standardized quality assurance of digital mammography and tomosynthesis with deep convolutional neural networksPatryk Hejduk, Raphael Sexauer, Carlotta Ruppert, et al.
Investigative Radiology|October 10, 2020
Classification of Mammographic Breast Microcalcifications Using a Deep Convolutional Neural Network: A BI-RADS-Based ApproachClaudio Schönenberger, Patryk Hejduk, Alexander Ciritsis, et al.
European Radiology|February 11, 2022
Fully automatic classification of automated breast ultrasound (ABUS) imaging according to BI-RADS using a deep convolutional neural networkPatryk Hejduk, Magda Marcon, Jan Unkelbach, et al.
Diagnostics (Basel, Switzerland)|January 21, 2022
Applied Machine Learning in Spiral Breast-CT: Can We Train a Deep Convolutional Neural Network for Automatic, Standardized and Observer Independent Classification of Breast Density?Anna Landsmann, Jann Wieler, Patryk Hejduk, et al.
Heliyon|August 13, 2021
Deep learning for the standardized classification of Ki-67 in vulva carcinoma: A feasibility studyMatthias Choschzick, Mariam Alyahiaoui, Alexander Ciritsis, et al.
European Radiology|March 1, 2023
Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networksRaphael Sexauer, Patryk Hejduk, Karol Borkowski, et al.
Clinical Imaging|January 5, 2023
Detection of microcalcifications in photon-counting dedicated breast-CT using a deep convolutional neural network: Proof of principleAnna Landsmann, Carlotta Ruppert, Karol Borkowski, et al.
Diagnostics (Basel, Switzerland)|June 24, 2022
Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural NetworkFrederik Abel, Anna Landsmann, Patryk Hejduk, et al.
Medicine|July 25, 2020
Fully automatic classification of breast MRI background parenchymal enhancement using a transfer learning approachKarol Borkowski, Cristina Rossi, Alexander Ciritsis, et al.
Plos One|June 20, 2015
Hydrogel Nanofilaments via Core-Shell ElectrospinningPaweł Nakielski, Sylwia Pawłowska, Filippo Pierini, et al.
Pageof 2