ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2019
Invertible Network for Classification and Biomarker Selection for ASD.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Robust, Generalizable, and Interpretable Artificial Intelligence-Derived Brain Fingerprints of Autism and Social Communication Symptom Severity.Biological psychiatry · 2022Article
- Deep Learning Assessment for Mining Important Medical Image Features of Various Modalities.Diagnostics (Basel, Switzerland) · 2022Article
- Identification of Autism Spectrum Disorder With Functional Graph Discriminative Network.Frontiers in neuroscience · 2021Article
- An Invertible Dynamic Graph Convolutional Network for Multi-Center ASD Classification.Frontiers in neuroscience · 2021Article
- Decision Explanation and Feature Importance for Invertible Networks.... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Determining biomarkers for autism spectrum disorder (ASD) is crucial to understanding its mechanisms. Recently deep learning methods have achieved success in the classification task of ASD using fMRI data. However, due to the black-box nature of most deep learning models, it's hard to perform biomarker selection and interpret model decisions. The recently proposed invertible networks can accurately reconstruct the input from its output, and have the potential to unravel the black-box representation. Therefore, we propose a novel method to classify ASD and identify biomarkers for ASD using the connectivity matrix calculated from fMRI as the input. Specifically, with invertible networks, we explicitly determine the decision boundary and the projection of data points onto the boundary. Like linear classifiers, the difference between a point and its projection onto the decision boundary can be viewed as the
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.