ArticleDiagnostics (Basel, Switzerland)2023
Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 2 of them syntheses that pooled it.
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Who cites it
45 citing papers in PubMed, 2 syntheses or guidelines pooled it, 180 citations in OpenAlex.
- Deep learning for early diagnosis of uveal melanoma: a systematic review and meta-analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Pooled it
- Demystifying Artificial Intelligence: A Systematic Review of Explainable Artificial Intelligence in Medical Imaging.Sensors (Basel, Switzerland) · 2026Pooled it
- YOLOv5 Attention Analysis for Anterior Eye Disease Classification: Grad-CAM++ Feature Importance and Cut-and-Paste Validation.Journal of imaging informatics in medicine · 2026Article
- Explainable Machine Learning for Predicting Dengue Recovery Duration: Insights from Multi-Center Clinical Data.Healthcare (Basel, Switzerland) · 2026Article
- Review
- Advanced computer-aided diagnosis for age-related macular degeneration: integrating segmentation and feature extraction for precise diagnosis.Scientific reports · 2026Article
- Counterfactual Reasoning for Mammogram Classification via Semantic Texture Masking.Journal of imaging informatics in medicine · 2026Article
- UbiQTree: Uncertainty quantification in XAI with tree ensembles.Patterns (New York, N.Y.) · 2026Article
- Machine learning empowered formulation design, optimization and characterization of nanoparticulate drug delivery systems: Current applications, challenges, and future perspectives.Acta pharmaceutica Sinica. B · 2026Review
- Revolutionizing endodontics: the impact and innovations of artificial intelligence.BMC oral health · 2026Review
- Transparency of medical artificial intelligence systems.Nature reviews bioengineering · 2026Article
- Interpretable machine learning for chronic kidney disease prediction: Insights from SHAP and LIME analyses.PloS one · 2026Article
- Deep learning framework for barcode localization and decoding using simulated UAV imagery.Scientific reports · 2025Article
- Letter to the editor: Reassessing predictive models for hypertension control in resource-constrained settings.International journal of cardiology. Cardiovascular risk and prevention · 2025Article
- Interpretable multiclassification machine learning models in predicting the extent of small cell lung cancer metastasis.Scientific reports · 2025Article
- Explainable artificial intelligence and ensemble learning for hepatocellular carcinoma classification: State of the art, performance, and clinical implications.World journal of hepatology · 2025Review
- Multi-output deep learning for high-frequency prediction of air and surface temperature in Kuwait.Scientific reports · 2025Article
- An artificial neural network approach for predicting infant mortality status in Ethiopia.BMC public health · 2025Article
- Explainable AI-Driven Analysis of Radiology Reports Using Text and Image Data: Experimental Study.JMIR formative research · 2025Article
- Analyzing Retinal Vessel Morphology in MS Using Interpretable AI on Deep Learning-Segmented IR-SLO Images.Bioengineering (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Retinoblastoma is a rare and aggressive form of childhood eye cancer that requires prompt diagnosis and treatment to prevent vision loss and even death. Deep learning models have shown promising results in detecting retinoblastoma from fundus images, but their decision-making process is often considered a "black box" that lacks transparency and interpretability. In this project, we explore the use of LIME and SHAP, two popular explainable AI techniques, to generate local and global explanations for a deep learning model based on InceptionV3 architecture trained on retinoblastoma and non-retinoblastoma fundus images. We collected and labeled a dataset of 400 retinoblastoma and 400 non-retinoblastoma images, split it into training, validation, and test sets, and trained the model using transfer learning from the pre-trained InceptionV3 model. We then applied LIME and SHAP to generate explanations for the model's predictions on the validation and test sets. Our results demonstrate that LIME and SHAP can effectively identify the regions and features in the input images that contribute the most to the model's predictions, providing valuable insights into the decision-making process of the deep learning model. In addition, the use of InceptionV3 architecture with spatial attention mechanism achieved high accuracy of 97% on the test set, indicating the potential of combining deep learning and explainable AI for improving retinoblastoma diagnosis and treatment.
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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.