Impact of network parameters on a U-Net based system for rectal cancer segmentation on MR images
Author:
Affiliation:
1. Polytechnic of Turin,Dept. of Electronics and Telecommunications,Torino,Italy
2. University of Turin and Candiolo Cancer Institute, FPO-IRCCS,Dept. of Surgical Science,Torino,Italy
Funder
AIRC
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9855895/9856402/09856529.pdf?arnumber=9856529
Reference22 articles.
1. Tuning hyperparameters of machine learning algorithms and deep neural networks using metaheuristics: A bioinformatics study on biomedical and biological cases
2. MRI-based automatic segmentation of rectal cancer using 2D U-Net on two independent cohorts
3. U-Net: Convolutional Networks for Biomedical Image Segmentation
4. Magnetic resonance imaging for clinical management of rectal cancer: Updated recommendations from the 2016 European Society of Gastrointestinal and Abdominal Radiology (ESGAR) consensus meeting
5. The efficacy of diffusion-weighted imaging for the detection of colorectal cancer;shinya;Hepatogastroenterology,2009
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