GradMask: Reduce Overfitting by Regularizing Saliency

Visualizing and Understanding Convolutional Networks

Original

Matthew D Zeiler and Rob Fergus · 1904

Earlier work this paper cites.

Simplifying neural networks by soft weight-sharing

Steven Nowlan and Geoffrey Hinton · 1992

Earlier work this paper cites.

Contractive Auto-Encoders: Explicit Invariance During Feature Extraction

Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, and Yoshua Bengio · 2011

Earlier work this paper cites.

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014

Earlier work this paper cites.

Dropout: A simple way to prevent neural networks from overfitting

Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov, and Morteza Analoui · 2014

Cited alongside, same era.

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Bjoern H. Menze et al · 2015

Cited alongside, same era.

Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross

2017

Cited alongside, same era.

Improved regularization of convolutional neural networks with cutout

Terrance Devries and Graham W. Taylor · 2017

Cited alongside, same era.

Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology

EJ Limkin, Roger Sun, Laurent Dercle, EI Zacharaki, Charlotte Robert, Sylvain Reuzé, Antoine Schernberg, Nikos Paragios, Eric Deutsch, and Charles Ferté · 2017

Later among the works it cites.

Confounding variables can degrade generalization performance of radiological deep learning models

John R. Zech, Marcus A. Badgeley, Manway Liu, Anthony B. Costa, Joseph J. Titano, and Eric K. Oermann · 2018

Later among the works it cites.

Gene Expression based Survival Prediction for Cancer Patients-A Topic Modeling Approach

Luke Kumar and Russell Greiner · 2019

Closest in time.