SoftSeg: Advantages of soft versus binary training for image segmentation

Author:

Gros CharleyORCID,Lemay AndreanneORCID,Cohen-Adad Julien

Funder

Canada Research Chairs

Canada First Research Excellence Fund

Canadian Institutes of Health Research

Institut de Valorisation des Données

Natural Sciences and Engineering Research Council of Canada

Canada Foundation for Innovation

Fonds de Recherche du Québec - Santé

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference62 articles.

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2. Agarap, A. F. (2018). Deep Learning using Rectified Linear Units (ReLU). In arXiv [cs.NE]. arXiv. https://doi.org/10.1109/MCSE.2007.55

3. A logarithmic opinion pool based STAPLE algorithm for the fusion of segmentations with associated reliability weights;Akhondi-Asl;IEEE Transactions on Medical Imaging,2014

4. Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions;Akkus;Journal of Digital Imaging,2017

5. PHiSeg: Capturing Uncertainty in Medical Image Segmentation;Baumgartner,2019

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