15 citations · 30 across the 11 of their papers we have counts for
11 papers
Beta quantile regression for robust estimation of uncertainty in the presence of outliers
Haleh Akrami, Omar Zamzam, Anand Joshi +2
Quantile Regression (QR) can be used to estimate aleatoric uncertainty in deep neural networks and can generate prediction intervals. Quantifying uncertainty is particularly import…
Toward Improved Generalization: Meta Transfer of Self-supervised Knowledge on Graphs
Wenhui Cui, Haleh Akrami, Anand A. Joshi +1
Despite the remarkable success achieved by graph convolutional networks for functional brain activity analysis, the heterogeneity of functional patterns and the scarcity of imaging…
Semi-supervised Learning with Robust Loss in Brain Segmentation
Hedong Zhang, Anand A. Joshi
In this work, we used a semi-supervised learning method to train deep learning model that can segment the brain MRI images. The semi-supervised model uses less labeled data, and th…
Learning from imperfect training data using a robust loss function: application to brain image segmentation
Haleh Akrami, Wenhui Cui, Anand A Joshi +1
Segmentation is one of the most important tasks in MRI medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis…
Semi-supervised Learning using Robust Loss
Wenhui Cui, Haleh Akrami, Anand A. Joshi +1
The amount of manually labeled data is limited in medical applications, so semi-supervised learning and automatic labeling strategies can be an asset for training deep neural netwo…
Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion Detection
Haleh Akrami, Anand Joshi, Sergul Aydore +1
Despite impressive state-of-the-art performance on a wide variety of machine learning tasks, deep learning methods can produce over-confident predictions, particularly with limited…
fMRI-Kernel Regression: A Kernel-based Method for Pointwise Statistical Analysis of rs-fMRI for Population Studies
Anand A. Joshi, Soyoung Choi, Haleh Akrami +1
Due to the spontaneous nature of resting-state fMRI (rs-fMRI) signals, cross-subject comparison and therefore, group studies of rs-fMRI are challenging. Most existing group compari…
Realistic head modeling of electromagnetic brain activity: An integrated Brainstorm pipeline from MRI data to the FEM solution
Takfarinas Medani, Juan Garcia-Prieto, Francois Tadel +6
Human brain activity generates scalp potentials (electroencephalography EEG), intracranial potentials (iEEG), and external magnetic fields (magnetoencephalography MEG), all capable…
Addressing Variance Shrinkage in Variational Autoencoders using Quantile Regression
Haleh Akrami, Anand A. Joshi, Sergul Aydore +1
Estimation of uncertainty in deep learning models is of vital importance, especially in medical imaging, where reliance on inference without taking into account uncertainty could l…
Robust Variational Autoencoder for Tabular Data with Beta Divergence
Haleh Akrami, Sergul Aydore, Richard M. Leahy +1
We propose a robust variational autoencoder with divergence for tabular data (RTVAE) with mixed categorical and continuous features. Variational autoencoders (VAE) and their va…
Robust Variational Autoencoder
Haleh Akrami, Anand A. Joshi, Jian Li +2
Machine learning methods often need a large amount of labeled training data. Since the training data is assumed to be the ground truth, outliers can severely degrade learned repres…