ArticlePattern recognition2019
Texture Analysis for Muscular Dystrophy Classification in MRI with Improved Class Activation Mapping.
Article in Pattern recognition, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 48 citations in OpenAlex.
- Automated Classification of Neuromuscular Diseases Using Thigh Muscle MRI With Model Interpretations.Journal of cachexia, sarcopenia and muscle · 2025Article
- Assessment of Age-Related Differences in Lower Leg Muscles Quality Using Radiomic Features of Magnetic Resonance Images.Journal of imaging informatics in medicine · 2025Article
- Machine learning-based radiomics using MRI to differentiate early-stage Duchenne and Becker muscular dystrophy in children.BMC musculoskeletal disorders · 2025Article
- The utility of muscle magnetic resonance imaging in idiopathic inflammatory myopathies: a scoping review.Frontiers in immunology · 2025Article
- Demystifying the black box: A survey on explainable artificial intelligence (XAI) in bioinformatics.Computational and structural biotechnology journal · 2025Review
- Convolutional Neural Network-Based Automated Segmentation of Skeletal Muscle and Subcutaneous Adipose Tissue on Thigh MRI in Muscular Dystrophy Patients.Journal of functional morphology and kinesiology · 2024Article
- Classification of Muscular Dystrophies from MR Images Improves Using the Swin Transformer Deep Learning Model.Bioengineering (Basel, Switzerland) · 2024Article
- Convolutional Neural Networks to Study Contrast-Enhanced Magnetic Resonance Imaging-Based Skeletal Calf Muscle Perfusion in Peripheral Artery Disease.The American journal of cardiology · 2024Article
- A machine learning-based approach to identify peripheral artery disease using texture features from contrast-enhanced magnetic resonance imaging.Magnetic resonance imaging · 2024Article
- SEMPAI: a Self-Enhancing Multi-Photon Artificial Intelligence for Prior-Informed Assessment of Muscle Function and Pathology.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Deep-Learning-Driven Quantification of Interstitial Fibrosis in Digitized Kidney Biopsies.The American journal of pathology · 2021Article
- A deep learning model for diagnosing dystrophinopathies on thigh muscle MRI images.BMC neurology · 2021Article
- Super resolution microscopy and deep learning identify Zika virus reorganization of the endoplasmic reticulum.Scientific reports · 2020Article
- MRI patterns of muscle involvement in type 2 and 3 spinal muscular atrophy patients.Journal of neurology · 2020Article
- Advancements in magnetic resonance imaging-based biomarkers for muscular dystrophy.Muscle & nerve · 2019Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
The muscular dystrophies are made up of a diverse group of rare genetic diseases characterized by progressive loss of muscle strength and muscle damage. Since there is no cure for muscular dystrophy and clinical outcome measures are limited, it is critical to assess the progression of MD objectively. Imaging muscle replacement by fibrofatty tissue has been shown to be a robust biomarker to monitor disease progression in DMD. In magnetic resonance imaging (MRI) data, specific texture patterns are found to correlate to certain MD subtypes and thus present a potential way for automatic assessment. In this paper, we first apply state-of-the-art convolutional neural networks (CNNs) to perform accurate MD image classification and then propose an effective visualization method to highlight the important image textures. With a dystrophic MRI dataset, we found that the best CNN model delivers an 91.7% classification accuracy, which significantly outperforms non-deep learning methods,
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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.