1 code implementation • Mathematics 2023 • Heewon Lee, Sangtae Ahn
We tested our proposed method not only on public datasets that typically exhibit balanced class distribution but also on private datasets that may have imbalanced class distribution.
1 code implementation • 28 Aug 2023 • Heewon Lee, Sangtae Ahn
We tested our proposed method not only on public datasets that typically exhibit balanced class distribution but also on custom datasets that may have imbalanced class distribution.
no code implementations • 9 Mar 2022 • Sangtae Ahn, Uri Wollner, Graeme McKinnon, Isabelle Heukensfeldt Jansen, Rafi Brada, Dan Rettmann, Ty A. Cashen, John Huston, J. Kevin DeMarco, Robert Y. Shih, Joshua D. Trzasko, Christopher J. Hardy, Thomas K. F. Foo
The trained model was evaluated on 3D MPRAGE brain scan data retrospectively-undersampled with a 10-fold acceleration, compared to a conventional parallel imaging method with a 2-fold acceleration.
no code implementations • 14 Jul 2021 • Arkadiusz Sitek, Sangtae Ahn, Evren Asma, Adam Chandler, Alvin Ihsani, Sven Prevrhal, Arman Rahmim, Babak Saboury, Kris Thielemans
Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) imaging applications.
1 code implementation • 5 Apr 2021 • Itzik Malkiel, Sangtae Ahn, Valentina Taviani, Anne Menini, Lior Wolf, Christopher J. Hardy
Recent accelerated MRI reconstruction models have used Deep Neural Networks (DNNs) to reconstruct relatively high-quality images from highly undersampled k-space data, enabling much faster MRI scanning.
Generative Adversarial Network Image-to-Image Translation +2
no code implementations • 24 Jun 2020 • Michael Rotman, Rafi Brada, Israel Beniaminy, Sangtae Ahn, Christopher J. Hardy, Lior Wolf
Motion artefacts created by patient motion during an MRI scan occur frequently in practice, often rendering the scans clinically unusable and requiring a re-scan.
no code implementations • 2 May 2019 • Itzik Malkiel, Sangtae Ahn, Valentina Taviani, Anne Menini, Lior Wolf, Christopher J. Hardy
Recent sparse MRI reconstruction models have used Deep Neural Networks (DNNs) to reconstruct relatively high-quality images from highly undersampled k-space data, enabling much faster MRI scanning.