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Found 7 papers, 2 papers with code
Solving the enigma: Deriving optimal explanations of deep networks
In this study, for the first time, we propose a novel framework designed to enhance the explainability of deep networks, by maximizing both the accuracy and the comprehensibility of the explanations.
An explainable three dimension framework to uncover learning patterns: A unified look in variable sulci recognition
With respect to this mathematical formulation, we propose a 3D explainability framework aimed at validating the outputs of deep learning networks in detecting the paracingulate sulcus an essential brain anatomical feature.
Deep Learning in current Neuroimaging: a multivariate approach with power and type I error control but arguable generalization ability
We found in the permutation test that CV and RUB methods offer a false positive rate close to the significance level and an acceptable statistical power (although lower using cross-validation).
A connection between the pattern classification problem and the General Linear Model for statistical inference
A connection between the General Linear Model (GLM) in combination with classical statistical inference and the machine learning (MLE)-based inference is described in this paper.
Single-participant structural connectivity matrices lead to greater accuracy in classification of participants than function in autism in MRI
In this work, we introduce a technique of deriving symmetric connectivity matrices from regional histograms of grey-matter volume estimated from T1-weighted MRIs.
Stochastic encoding of graphs in deep learning allows for complex analysis of gender classification in resting-state and task functional brain networks from the UK Biobank
We applied our method to resting-state and task data from the UK BioBank, using two visualization techniques to measure the salience of three brain networks involved in task- and resting-states, and their interaction.
Ensemble Deep Learning on Large, Mixed-Site fMRI Datasets in Autism and Other Tasks
Employing class-balancing to build a training set, we trained 3$\times$300 modified CNNs in an ensemble model to classify fMRI connectivity matrices with overall AUROCs of 0. 6774, 0. 7680, and 0. 9222 for ASD vs TD, gender, and task vs rest, respectively.
