ArticleMachine learning in medical imaging. MLMI (Workshop)2014
Deep Learning for Cerebellar Ataxia Classification and Functional Score Regression.
Article in Machine learning in medical imaging. MLMI (Workshop), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The trial behind it
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Who cites it
8 citing papers in PubMed, 32 citations in OpenAlex.
- Development of a cerebellar ataxia diagnosis model using conditional GAN-based synthetic data generation for visuomotor adaptation task.BMC medical informatics and decision making · 2024Article
- Heterogeneous digital biomarker integration out-performs patient self-reports in predicting Parkinson's disease.Communications biology · 2022Article
- A cerebellum inspired spiking neural network as a multi-model for pattern classification and robotic trajectory prediction.Frontiers in neuroscience · 2022Article
- Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network.Applied bionics and biomechanics · 2021Article
- Diagnosis of Alzheimer's Disease Severity with fMRI Images Using Robust Multitask Feature Extraction Method and Convolutional Neural Network (CNN).Computational and mathematical methods in medicine · 2021Article
- ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data.Frontiers in neuroinformatics · 2019Article
- Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images.NeuroImage · 2018Article
- Landmark Based Shape Analysis for Cerebellar Ataxia Classification and Cerebellar Atrophy Pattern Visualization.Proceedings of SPIE--the International Society for Optical Engineering · 2016Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Cerebellar ataxia is a progressive neuro-degenerative disease that has multiple genetic versions, each with a characteristic pattern of anatomical degeneration that yields distinctive motor and cognitive problems. Studying this pattern of degeneration can help with the diagnosis of disease subtypes, evaluation of disease stage, and treatment planning. In this work, we propose a learning framework using MR image data for discriminating a set of cerebellar ataxia types and predicting a disease related functional score. We address the difficulty in analyzing high-dimensional image data with limited training subjects by: 1) training weak classifiers/regressors on a set of image subdomains separately, and combining the weak classifier/regressor outputs to make the decision; 2) perturbing the image subdomain to increase the training samples; 3) using a deep learning technique called the stacked auto-encoder to develop highly representative feature vectors of the input data. Experiments show that our approach can reliably classify between one of four categories (healthy control and three types of ataxia), and predict the functional staging score for ataxia.
Identifiers
What OpenQuestion holds
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.