3 papers
eess.IV2024
ToNNO: Tomographic Reconstruction of a Neural Network's Output for Weakly Supervised Segmentation of 3D Medical Images
Marius Schmidt-Mengin, Alexis Benichoux, Shibeshih Belachew +2
Annotating lots of 3D medical images for training segmentation models is time-consuming. The goal of weakly supervised semantic segmentation is to train segmentation models without…
cs.CV2013
Discriminative Parameter Estimation for Random Walks Segmentation
Pierre-Yves Baudin, Danny Goodman, Puneet Kumar +4
The Random Walks (RW) algorithm is one of the most e - cient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate…
cs.CV2013
Discriminative Parameter Estimation for Random Walks Segmentation: Technical Report
Pierre-Yves Baudin, Danny Goodman, Puneet Kumar +4
The Random Walks (RW) algorithm is one of the most e - cient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate…