45 citations · 47 across the 4 of their papers we have counts for
4 papers
Full-Head Segmentation of MRI with Abnormal Brain Anatomy: Model and Data Release
Andrew M Birnbaum, Adam Buchwald, Peter Turkeltaub +7
Purpose: The goal of this work was to develop a deep network for whole-head segmentation including clinical MRIs with abnormal anatomy, and compile the first public benchmark datas…
Predicting breast cancer with AI for individual risk-adjusted MRI screening and early detection
Lukas Hirsch, Yu Huang, Hernan A. Makse +7
Women with an increased life-time risk of breast cancer undergo supplemental annual screening MRI. We propose to predict the risk of developing breast cancer within one year based…
Radiologist-level Performance by Using Deep Learning for Segmentation of Breast Cancers on MRI Scans
Lukas Hirsch, Yu Huang, Shaojun Luo +19
Purpose: To develop a deep network architecture that would achieve fully automated radiologist-level segmentation of cancers at breast MRI. Materials and Methods: In this retrospec…
Segmentation of MRI head anatomy using deep volumetric networks and multiple spatial priors
Lukas Hirsch, Yu Huang, Lucas C Parra
Purpose: Conventional automated segmentation of the head anatomy in MRI distinguishes different brain and non-brain tissues based on image intensities and prior tissue probability…