A Study on the Interpretability of Diabetic Retinopathy Diagnostic Models.

Zerui Zhang, Hongbo Zhao, Li Dong, Lin Luo, Hao Wang

Abstract read

In one paragraph

Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Zerui ZhangSchool of Bioengineering, Chongqing University, Chongqing 400044, China.

Hongbo ZhaoSchool of Bioengineering, Chongqing University, Chongqing 400044, China.

Li DongBeijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China.

Lin LuoCollege of Engineering, Peking University, Beijing 100871, China.

Hao WangInstitute for Medical Device Control, National Institutes for Food and Drug Control, Beijing 102629, China.ORCID 0000-0003-4094-7306

Funding

National Institutes for Food and Drug Control key technology funding GJJS-2022-3-1

6 · The paper itself

Abstract

This study focuses on the interpretability of diabetic retinopathy classification models. Seven widely used interpretability methods-Gradient, SmoothGrad, Integrated Gradients, SHAP, DeepLIFT, Grad-CAM++, and ScoreCAM-are applied to assess the interpretability of four representative deep learning architectures, VGG, ResNet, DenseNet, and EfficientNet, on fundus images. Through saliency map visualization, perturbation curve analysis, and trend correlation analysis, combined with four quantitative metrics-saliency map entropy, AOPC score, Recall, and Dice coefficient-the interpretability performance of the models is comprehensively assessed from both qualitative and quantitative perspectives. The results show that model architecture greatly influences interpretability quality: models with simpler structures and clearer feature extraction paths (such as VGG) perform better in terms of interpretability, while deeper or lightweight architectures exhibit certain limitations.

Indexed as

diabetic retinopathy classification modelsfundus imagesinterpretability evaluationmodel interpretability

Identifiers

PMID41301187

DOI10.3390/bioengineering12111231

PMCPMC12649958

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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.