DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data

20 October 2023

Papers citing "DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data"

6 / 6 papers shown

Title
UNETR: Transformers for 3D Medical Image Segmentation Ali Hatamizadeh Yucheng Tang Vishwesh Nath Dong Yang Andriy Myronenko Bennett Landman H. Roth Daguang Xu ViT MedIm 156 1,567 0 18 Mar 2021
Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering Wonjik Kim Asako Kanezaki Masayuki Tanaka 48 210 0 20 Jul 2020
Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge Yue Sun Kun Gao Zhengwang Wu Zhihao Lei Ying Wei ... Weili Lin V. Jewells Gang Li Dinggang Shen Liwen Wang 49 71 0 04 Jul 2020
Deep Knockoffs Yaniv Romano Matteo Sesia Emmanuel J. Candès BDL 56 139 0 16 Nov 2018
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge Spyridon Bakas M. Reyes Andras Jakab Stefan Bauer Markus Rempfler ... Jayashree Kalpathy-Cramer Keyvan Farahani Christos Davatzikos Koen van Leemput Bjoern Menze 109 1,623 0 05 Nov 2018
Multiple Testing for Neuroimaging via Hidden Markov Random Field Hai Shu B. Nan R. Koeppe 41 34 0 04 Apr 2014