ArticleInternational journal of computer assisted radiology and surgery2022
Explainability of deep neural networks for MRI analysis of brain tumors.
Article in International journal of computer assisted radiology and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Demystifying Artificial Intelligence: A Systematic Review of Explainable Artificial Intelligence in Medical Imaging.Sensors (Basel, Switzerland) · 2026Pooled it
- ETDACVO: Structural-Fidelity-Aware Evolutionary Co-Optimization for Robust and Explainable Brain Tumor MRI Classification.Biomedicines · 2026Article
- Surface-based brain age index reflects hematological impacts on cognitive development in children with beta-thalassemia major.iScience · 2026Article
- Detection of Interpretable and Fine-Grained Brain Tumor Magnetic Resonance Imaging Based on Progressive Pruning: Machine Learning Model Development and Validation Study.JMIR medical informatics · 2026Article
- Review
- QuantumNeuroXAI: a quantum-inspired deep learning framework with explainability for brain signal analysis and neurological disorder detection.Scientific reports · 2026Article
- A comprehensive review of explainable artificial intelligence in healthcare methods, evaluation, and clinical integration.iScience · 2026Review
- An Optimized Strategy for Brain Tumor Classification Using SO(3) Equivariant Graph Neural Networks with Snow Geese Algorithm in MRI Imaging.Journal of molecular neuroscience : MN · 2026Article
- A Robust ConvNeXt-Based Framework for Efficient, Generalizable, and Explainable Brain Tumor Classification on MRI.Bioengineering (Basel, Switzerland) · 2026Article
- Mod-SE(2): a geometric deep learning framework for brain tumor classification and segmentation in MRI images.Journal of biomedical science · 2026Article
- Global-local feature fusion: a robust hybrid deep learning model for multiclass brain tumor classification with Grad-CAM++ interpretation.Frontiers in genetics · 2026Article
- Hybrid Convolutional-Gated Recurrent Neural Network for Robust Mobile Health Activities Classification.International journal of telemedicine and applications · 2026Article
- Explainable 3D vision transformer framework for detecting brain anomalies in neuropsychiatric subtypes of Parkinson's disease.Frontiers in neuroinformatics · 2026Article
- Article
- Boosting brain tumor detection with an optimized ResNet and explainability via Grad-CAM and LIME.Brain informatics · 2025Article
- Efficient hybrid fuzzy weighted 3D FCNN with TSO PSO optimization for accurate multi modal MRI brain tumor classification.Scientific reports · 2025Article
- A Web-Deployed, Explainable AI System for Comprehensive Brain Tumor Diagnosis.Neurology international · 2025Article
- Explainable AI in early autism detection: a literature review of interpretable machine learning approaches.Discover mental health · 2025Review
- Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
purposeArtificial intelligence (AI), in particular deep neural networks, has achieved remarkable results for medical image analysis in several applications. Yet the lack of explainability of deep neural models is considered the principal restriction before applying these methods in clinical practice.
methodsIn this study, we propose a NeuroXAI framework for explainable AI of deep learning networks to increase the trust of medical experts. NeuroXAI implements seven state-of-the-art explanation methods providing visualization maps to help make deep learning models transparent.
resultsNeuroXAI has been applied to two applications of the most widely investigated problems in brain imaging analysis, i.e., image classification and segmentation using magnetic resonance (MR) modality. Visual attention maps of multiple XAI methods have been generated and compared for both applications. Another experiment demonstrated that NeuroXAI can provide information flow visualization on internal layers of a segmentation CNN.
conclusionDue to its open architecture, ease of implementation, and scalability to new XAI methods, NeuroXAI could be utilized to assist radiologists and medical professionals in the detection and diagnosis of brain tumors in the clinical routine of cancer patients. The code of NeuroXAI is publicly accessible at https://github.com/razeineldin/NeuroXAI .
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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.