AI models offer interpretable diabetic retinopathy grading with visual and text explanations

Researchers have developed a new method for grading diabetic retinopathy (DR) that combines deep learning models with interpretable explanations. The approach uses CNN and transformer architectures, achieving a QWK score of up to 0.934 through weighted soft voting ensembling. For interpretability, the study generated visual attribution maps using Grad-CAM++ and textual rationales from vision-language models, aiming to provide clinically meaningful insights from retinal images. AI

影响 Enhances interpretability of medical AI models, potentially improving clinical trust and adoption for DR grading.

排序理由 This is a research paper detailing a new methodology for medical image analysis and interpretability.

在 arXiv cs.CV 阅读 →

• APTOS 2019
• arXiv
• CNN
• ConvNeXt-Tiny
• Diabetic Retinopathy
• Grad-CAM++
• ResNet-50
• Transformer
• Vision-Language Models

• 论文
• 模型发布

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