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Matthew D DiFranco

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Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society|January 29, 2011
Ensemble based system for whole-slide prostate cancer probability mapping using color texture featuresMatthew D DiFranco, Gillian O'Hurley, Elaine W Kay, et al.
Rofo : Fortschritte Auf Dem Gebiete Der Rontgenstrahlen Und Der Nuklearmedizin|December 8, 2023
Applicability and robustness of an artificial intelligence-based assessment for Greulich and Pyle bone age in a German cohortJohanna Pape, Franz Wolfgang Hirsch, Oliver Johannes Deffaa, et al.
Clinical Biomechanics (Bristol, Avon)|February 29, 2020
Biomechanical comparison of knotless vs. knotted suture anchors in the acetabular rim with respect to bone densityEmir Benca, Constantin E Dlaska, Matthew D DiFranco, et al.
Plos One|April 25, 2014
An intra-individual comparison of MRI, [18F]-FET and [18F]-FLT PET in patients with high-grade gliomasMartha Nowosielski, Matthew D DiFranco, Daniel Putzer, et al.
Physics in Medicine and Biology|May 21, 2019
Impact of intensity discretization on textural indices of [<sup>18</sup>F]FDG-PET tumour heterogeneity in lung cancer patientsAttila Forgács, Monika Béresová, Ildikó Garai, et al.
Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine|February 27, 2016
Reproducibility of MRI Dixon-Based Attenuation Correction in Combined PET/MR with Applications for Lean Body Mass EstimationIvo Rausch, Petra Rust, Matthew D DiFranco, et al.
Skeletal Radiology|November 13, 2021
Fully automated deep learning for knee alignment assessment in lower extremity radiographs: a cross-sectional diagnostic studySebastian Simon, Gilbert M Schwarz, Alexander Aichmair, et al.
Bone & Joint Open|November 14, 2022
Artificial intelligence-generated hip radiological measurements are fast and adequate for reliable assessment of hip dysplasia : an external validation studyHolden Archer, Seth Reine, Ahmed Alshaikhsalama, et al.
Rheumatology (Oxford, England)|December 29, 2022
Artificial intelligence in osteoarthritis: repair by knee joint distraction shows association of pain, radiographic and immunological outcomesMylène P Jansen, Christoph Salzlechner, Eleanor Barnes, et al.
Plos One|October 14, 2016
A Study on the Basic Criteria for Selecting Heterogeneity Parameters of F18-FDG PET ImagesAttila Forgacs, Hermann Pall Jonsson, Magnus Dahlbom, et al.
Pageof 2

Showing results (1-10 of 12) with videos related to

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Pageof 2
Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society|January 29, 2011
Ensemble based system for whole-slide prostate cancer probability mapping using color texture featuresMatthew D DiFranco, Gillian O'Hurley, Elaine W Kay, et al.
Rofo : Fortschritte Auf Dem Gebiete Der Rontgenstrahlen Und Der Nuklearmedizin|December 8, 2023
Applicability and robustness of an artificial intelligence-based assessment for Greulich and Pyle bone age in a German cohortJohanna Pape, Franz Wolfgang Hirsch, Oliver Johannes Deffaa, et al.
Clinical Biomechanics (Bristol, Avon)|February 29, 2020
Biomechanical comparison of knotless vs. knotted suture anchors in the acetabular rim with respect to bone densityEmir Benca, Constantin E Dlaska, Matthew D DiFranco, et al.
Plos One|April 25, 2014
An intra-individual comparison of MRI, [18F]-FET and [18F]-FLT PET in patients with high-grade gliomasMartha Nowosielski, Matthew D DiFranco, Daniel Putzer, et al.
Physics in Medicine and Biology|May 21, 2019
Impact of intensity discretization on textural indices of [<sup>18</sup>F]FDG-PET tumour heterogeneity in lung cancer patientsAttila Forgács, Monika Béresová, Ildikó Garai, et al.
Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine|February 27, 2016
Reproducibility of MRI Dixon-Based Attenuation Correction in Combined PET/MR with Applications for Lean Body Mass EstimationIvo Rausch, Petra Rust, Matthew D DiFranco, et al.
Skeletal Radiology|November 13, 2021
Fully automated deep learning for knee alignment assessment in lower extremity radiographs: a cross-sectional diagnostic studySebastian Simon, Gilbert M Schwarz, Alexander Aichmair, et al.
Bone & Joint Open|November 14, 2022
Artificial intelligence-generated hip radiological measurements are fast and adequate for reliable assessment of hip dysplasia : an external validation studyHolden Archer, Seth Reine, Ahmed Alshaikhsalama, et al.
Rheumatology (Oxford, England)|December 29, 2022
Artificial intelligence in osteoarthritis: repair by knee joint distraction shows association of pain, radiographic and immunological outcomesMylène P Jansen, Christoph Salzlechner, Eleanor Barnes, et al.
Plos One|October 14, 2016
A Study on the Basic Criteria for Selecting Heterogeneity Parameters of F18-FDG PET ImagesAttila Forgacs, Hermann Pall Jonsson, Magnus Dahlbom, et al.
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