Uncertainty-driven refinement of tumor-core segmentation using 3D-to-2D networks with label uncertainty

11 December 2020

Roland Wiest

Papers citing "Uncertainty-driven refinement of tumor-core segmentation using 3D-to-2D networks with label uncertainty"

2 / 2 papers shown

Title
Panoptica -- instance-wise evaluation of 3D semantic and instance segmentation maps Florian Kofler Hendrik Möller Josef A. Buchner Ezequiel de la Rosa Ivan Ezhov ... Stefan K. Ehrlich Annika Reinke Bjoern H. Menze Benedikt Wiestler Marie Piraud ISeg 22 7 0 05 Dec 2023
Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis Benjamin Lambert Florence Forbes A. Tucholka Senan Doyle Harmonie Dehaene M. Dojat 34 80 0 05 Oct 2022