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Benchmarking CNN on 3D Anatomical Brain MRI: Architectures, Data
  Augmentation and Deep Ensemble Learning

Benchmarking CNN on 3D Anatomical Brain MRI: Architectures, Data Augmentation and Deep Ensemble Learning

2 June 2021
Benoit Dufumier
Pietro Gori
Ilaria Battaglia
J. Victor
Antoine Grigis
Edouard Duchesnay
    3DV
ArXivPDFHTML

Papers citing "Benchmarking CNN on 3D Anatomical Brain MRI: Architectures, Data Augmentation and Deep Ensemble Learning"

7 / 7 papers shown
Title
Few-Shot Classification of Autism Spectrum Disorder using Site-Agnostic
  Meta-Learning and Brain MRI
Few-Shot Classification of Autism Spectrum Disorder using Site-Agnostic Meta-Learning and Brain MRI
Nikhil J. Dhinagar
Vignesh Santhalingam
Katherine E. Lawrence
Emily Laltoo
Paul M. Thompson
23
3
0
14 Mar 2023
Unsupervised Learning of Unbiased Visual Representations
Unsupervised Learning of Unbiased Visual Representations
C. Barbano
Enzo Tartaglione
Marco Grangetto
SSL
CML
OOD
39
1
0
26 Apr 2022
TorchIO: A Python library for efficient loading, preprocessing,
  augmentation and patch-based sampling of medical images in deep learning
TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning
Fernando Pérez-García
Rachel Sparks
Sébastien Ourselin
MedIm
LM&MA
147
427
0
09 Mar 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
276
5,695
0
05 Dec 2016
Aggregated Residual Transformations for Deep Neural Networks
Aggregated Residual Transformations for Deep Neural Networks
Saining Xie
Ross B. Girshick
Piotr Dollár
Zhuowen Tu
Kaiming He
321
10,237
0
16 Nov 2016
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image
  Segmentation
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Vijay Badrinarayanan
Alex Kendall
R. Cipolla
SSeg
454
15,657
0
02 Nov 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
287
9,167
0
06 Jun 2015
1