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Nicholas Konz

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

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Medical Image Analysis|May 18, 2023
Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completionNicholas Konz, Haoyu Dong, Maciej A Mazurowski
Journal of Digital Imaging|December 21, 2022
Deep Learning for Breast MRI Style Transfer with Limited Training DataShixing Cao, Nicholas Konz, James Duncan, et al.
Scientific Reports|May 14, 2021
A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesisAlbert Swiecicki, Nicholas Konz, Mateusz Buda, et al.
IEEE Transactions on Medical Imaging|September 11, 2023
SWSSL: Sliding Window-Based Self-Supervised Learning for Anomaly Detection in High-Resolution ImagesHaoyu Dong, Yifan Zhang, Hanxue Gu, et al.
Medical Image Analysis|August 18, 2023
Segment anything model for medical image analysis: An experimental studyMaciej A Mazurowski, Haoyu Dong, Hanxue Gu, et al.
IEEE Journal of Biomedical and Health Informatics|December 24, 2025
GuidedMorph: Two-Stage Deformable Registration for Breast MRIYaqian Chen, Hanxue Gu, Haoyu Dong, et al.
International Journal of Computer Assisted Radiology and Surgery|April 30, 2026
The impact of scanner domain shift on deep learning performance in medical imaging: an experimental studyBrian Guo, Darui Lu, Gregory Szumel, et al.
IEEE Transactions on Medical Imaging|November 3, 2025
Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2Yuwen Chen, Zafer Yildiz, Qihang Li, et al.
Medical Image Analysis|January 29, 2026
Fréchet radiomic distance (FRD): A versatile metric for comparing medical imaging datasetsNicholas Konz, Richard Osuala, Preeti Verma, et al.
JAMA Network Open|February 23, 2023
A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast TomosynthesisNicholas Konz, Mateusz Buda, Hanxue Gu, et al.
Pageof 1

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

Sort By:
Pageof 1
Medical Image Analysis|May 18, 2023
Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completionNicholas Konz, Haoyu Dong, Maciej A Mazurowski
Journal of Digital Imaging|December 21, 2022
Deep Learning for Breast MRI Style Transfer with Limited Training DataShixing Cao, Nicholas Konz, James Duncan, et al.
Scientific Reports|May 14, 2021
A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesisAlbert Swiecicki, Nicholas Konz, Mateusz Buda, et al.
IEEE Transactions on Medical Imaging|September 11, 2023
SWSSL: Sliding Window-Based Self-Supervised Learning for Anomaly Detection in High-Resolution ImagesHaoyu Dong, Yifan Zhang, Hanxue Gu, et al.
Medical Image Analysis|August 18, 2023
Segment anything model for medical image analysis: An experimental studyMaciej A Mazurowski, Haoyu Dong, Hanxue Gu, et al.
IEEE Journal of Biomedical and Health Informatics|December 24, 2025
GuidedMorph: Two-Stage Deformable Registration for Breast MRIYaqian Chen, Hanxue Gu, Haoyu Dong, et al.
International Journal of Computer Assisted Radiology and Surgery|April 30, 2026
The impact of scanner domain shift on deep learning performance in medical imaging: an experimental studyBrian Guo, Darui Lu, Gregory Szumel, et al.
IEEE Transactions on Medical Imaging|November 3, 2025
Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2Yuwen Chen, Zafer Yildiz, Qihang Li, et al.
Medical Image Analysis|January 29, 2026
Fréchet radiomic distance (FRD): A versatile metric for comparing medical imaging datasetsNicholas Konz, Richard Osuala, Preeti Verma, et al.
JAMA Network Open|February 23, 2023
A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast TomosynthesisNicholas Konz, Mateusz Buda, Hanxue Gu, et al.
Pageof 1