Sept. 23, 2022, 1:11 a.m. | Benjamin Lambert, Florence Forbes, Senan Doyle, Alan Tucholka, Michel Dojat

cs.LG updates on arXiv.org arxiv.org

Deep neural networks have become the gold-standard approach for the automated
segmentation of 3D medical images. Their full acceptance by clinicians remains
however hampered by the lack of intelligible uncertainty assessment of the
provided results. Most approaches to quantify their uncertainty, such as the
popular Monte Carlo dropout, restrict to some measure of uncertainty in
prediction at the voxel level. In addition not to be clearly related to genuine
medical uncertainty, this is not clinically satisfying as most objects of …

arxiv brain prediction trust uncertainty voxel

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Applied Scientist, Control Stack, AWS Center for Quantum Computing

@ Amazon.com | Pasadena, California, USA

Specialist Marketing with focus on ADAS/AD f/m/d

@ AVL | Graz, AT

Machine Learning Engineer, PhD Intern

@ Instacart | United States - Remote

Supervisor, Breast Imaging, Prostate Center, Ultrasound

@ University Health Network | Toronto, ON, Canada

Senior Manager of Data Science (Recommendation Science)

@ NBCUniversal | New York, NEW YORK, United States