All Points Considered: A Maximum Likelihood Method for Motion Recovery. - Daniel Keren; Ilan Shimshoni; Liran Goshen; Michael Werman (Theoretical Foundations of Computer Vision, 2002)

Recovery of Epipolar Geometry as a Manifold Fitting Problem. - Liran Goshen; Ilan Shimshoni; P. Anandan 001; Daniel Keren (ICCV, 2003)

Motion Recovery by Integrating over the Joint Image Manifold. - Liran Goshen; Ilan Shimshoni; P. Anandan 001; Daniel Keren (Int. J. Comput. Vis., 2005)

Guided Sampling via Weak Motion Models and Outlier Sample Generation for Epipolar Geometry Estimation. - Liran Goshen; Ilan Shimshoni (Int. J. Comput. Vis., 2008)

Balanced Exploration and Exploitation Model Search for Efficient Epipolar Geometry Estimation. - Liran Goshen; Ilan Shimshoni (IEEE Trans. Pattern Anal. Mach. Intell., 2008)

Simultaneous Multi-phase Coronary CT Angiography Analysis for Coronary Artery Disease Evaluation. - Yechiel Lamash; Moti Freiman; Liran Goshen (CLIP@MICCAI, 2014)

Learning Patient-Specific Lumped Models for Interactive Coronary Blood Flow Simulations. - Hannes Nickisch; Yechiel Lamash; Sven Prevrhal; Moti Freiman; Mani Vembar; Liran Goshen; Holger Schmitt (MICCAI (2), 2015)

Automatic coronary lumen segmentation with partial volume modeling improves lesions' hemodynamic significance assessment. - Moti Freiman; Yechiel Lamash; Guy Gilboa; Hannes Nickisch; Sven Prevrhal; Holger Schmitt; Mani Vembar; Liran Goshen (Image Processing, 2016)

Learning a Sparse Database for Patch-Based Medical Image Segmentation. - Moti Freiman; Hannes Nickisch; Holger Schmitt; Pál Maurovich-Horvat; Patrick Donnelly; Mani Vembar; Liran Goshen (Patch-MI@MICCAI, 2017)

Unsupervised Abnormality Detection through Mixed Structure Regularization (MSR) in Deep Sparse Autoencoders. - Moti Freiman; Ravindra Manjeshwar; Liran Goshen (CoRR, 2019)

Improving CCTA based lesions' hemodynamic significance assessment by accounting for partial volume modeling in automatic coronary lumen segmentation. - Moti Freiman; Hannes Nickisch; Sven Prevrhal; Holger Schmitt; Mani Vembar; Pál Maurovich-Horvat; Patrick Donnelly; Liran Goshen (CoRR, 2019)

Non Parametric Data Augmentations Improve Deep-Learning based Brain Tumor Segmentation. - Hadas Ben-Atya; Ori Rajchert; Liran Goshen; Moti Freiman (CoRR, 2021)