1 code implementation • 27 Jun 2023 • Yunsung Chung, Chanho Lim, Chao Huang, Nassir Marrouche, Jihun Hamm
Specifically, we leverage the contrastive loss to learn representations of both the foreground and background regions in the images.
no code implementations • 7 Sep 2020 • Anupama Goparaju, Alexandre Bone, Nan Hu, Heath B. Henninger, Andrew E. Anderson, Stanley Durrleman, Matthijs Jacxsens, Alan Morris, Ibolya Csecs, Nassir Marrouche, Shireen Y. Elhabian
Statistical shape modeling (SSM) is widely used in biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes.
no code implementations • 6 Mar 2019 • Tim Sodergren, Riddhish Bhalodia, Ross Whitaker, Joshua Cates, Nassir Marrouche, Shireen Elhabian
Here, we propose a maximum-a-posteriori formulation that relies on a generative image model by incorporating both local intensity and global shape priors.
no code implementations • 3 Oct 2018 • Anupama Goparaju, Ibolya Csecs, Alan Morris, Evgueni Kholmovski, Nassir Marrouche, Ross Whitaker, Shireen Elhabian
Statistical shape modeling (SSM) has proven useful in many areas of biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes.
no code implementations • 30 Sep 2018 • Riddhish Bhalodia, Anupama Goparaju, Tim Sodergren, Alan Morris, Evgueni Kholmovski, Nassir Marrouche, Joshua Cates, Ross Whitaker, Shireen Elhabian
In this paper, we propose a machine learning approach that uses deep networks to estimate AF recurrence by predicting shape descriptors directly from MRI images, with NO image pre-processing involved.