ArticlePloS one2024
Explainable ensemble learning method for OCT detection with transfer learning.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed.
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- A benchmark study of vision and pathology foundation models for computational pathology.Nature communications · 2026Article
- Pyramid Xception network for accurate detection of abdominal abnormalities in CT scans.Scientific reports · 2026Article
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- An explainable meta-learned hybrid CNN-transformer model with dual attention for leukemia diagnosis from peripheral blood smears.Scientific reports · 2026Article
- Robust unsupervised outlier detection in IoT using contrastive learning-driven autoencoders.Scientific reports · 2026Article
- Interpretable CRAM‑Enhanced Lightweight Dual‑Branch CNN for Real‑Time Breast Cancer Histopathology in Internet‑of‑Medical‑Things Environments.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Deep learning-based breast cancer detection with customized ensemble attention.Scientific reports · 2026Article
- Transformer-enhanced deep ensemble for multi-class liver disease classification using computed tomography images.Scientific reports · 2026Article
- FMCL: a transformer-based feature-map classifier learning approach for enhanced brain tumor detection in MRI.Scientific reports · 2026Article
- An Intelligent Hybrid Ensemble Model for Early Detection of Breast Cancer in Multidisciplinary Healthcare Systems.Diagnostics (Basel, Switzerland) · 2026Article
- A hybrid approach for facial parsing using transfer learning.Scientific reports · 2026Article
- A transfer learning-based approach for automated bone fracture classification in X-ray imaging.Therapeutic advances in musculoskeletal disease · 2026Article
- Breast cancer classification based on microcalcifications using dual branch vision transformer fusion.Scientific reports · 2025Article
- Deep learning-based joint analysis of diabetic retinopathy and glaucoma in retinal fundus images.Scientific reports · 2025Article
- Optimized federated learning framework with RegNetZ and Swin-Transformer for multimodal pancreatic cancer detection1.Scientific reports · 2025Article
- HyperFusionNet combines vision transformer for early melanoma detection and precise lesion segmentation.Scientific reports · 2025Article
- Artificial intelligence-driven diagnosis for age-related macular degeneration bridging pathology and engineering: a survey.International ophthalmology · 2025Review
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5 authors.
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Abstract
The accuracy and interpretability of artificial intelligence (AI) are crucial for the advancement of optical coherence tomography (OCT) image detection, as it can greatly reduce the manual labor required by clinicians. By prioritizing these aspects during development and application, we can make significant progress towards streamlining the clinical workflow. In this paper, we propose an explainable ensemble approach that utilizes transfer learning to detect fundus lesion diseases through OCT imaging. Our study utilized a publicly available OCT dataset consisting of normal subjects, patients with dry age-related macular degeneration (AMD), and patients with diabetic macular edema (DME), each with 15 samples. The impact of pre-trained weights on the performance of individual networks was first compared, and then these networks were ensemble using majority soft polling. Finally, the features learned by the networks were visualized using Grad-CAM and CAM. The use of pre-trained ImageNet weights improved the performance from 68.17% to 92.89%. The ensemble model consisting of the three CNN models with pre-trained parameters loaded performed best, correctly distinguishing between AMD patients, DME patients and normal subjects 100% of the time. Visualization results showed that Grad-CAM could display the lesion area more accurately. It is demonstrated that the proposed approach could have good performance of both accuracy and interpretability in retinal OCT image detection.
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