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Learning from imperfect training data using a robust loss function: application to brain image segmentation


Aug 08, 2022
Haleh Akrami, Wenhui Cui, Anand A Joshi, Richard M. Leahy

* 2 pages short paper. Please see https://github.com/ajoshiusc/brainseg for the source code 

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Semi-supervised Learning using Robust Loss


Mar 03, 2022
Wenhui Cui, Haleh Akrami, Anand A. Joshi, Richard M. Leahy


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Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion Detection


Sep 20, 2021
Haleh Akrami, Anand Joshi, Sergul Aydore, Richard Leahy

* arXiv admin note: substantial text overlap with arXiv:2010.09042 

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fMRI-Kernel Regression: A Kernel-based Method for Pointwise Statistical Analysis of rs-fMRI for Population Studies


Dec 13, 2020
Anand A. Joshi, Soyoung Choi, Haleh Akrami, Richard M. Leahy


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Addressing Variance Shrinkage in Variational Autoencoders using Quantile Regression


Oct 18, 2020
Haleh Akrami, Anand A. Joshi, Sergul Aydore, Richard M. Leahy


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Robust Variational Autoencoder for Tabular Data with Beta Divergence


Jun 16, 2020
Haleh Akrami, Sergul Aydore, Richard M. Leahy, Anand A. Joshi


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A Pre-defined Sparse Kernel Based Convolution for Deep CNNs


Oct 16, 2019
Souvik Kundu, Saurav Prakash, Haleh Akrami, Peter A. Beerel, Keith M. Chugg

* 8 pages, 12 figures, Computer vision 

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A Pre-defined Sparse Kernel Based Convolutionfor Deep CNNs


Oct 02, 2019
Souvik Kundu, Saurav Prakash, Haleh Akrami, Peter A. Beerel, Keith M. Chugg

* 8 pages, 12 figures, Computer vision 

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Robust Variational Autoencoder


May 23, 2019
Haleh Akrami, Anand A. Joshi, Jian Li, Richard M. Leahy


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