ArticleNature communications2023
Uncertainty-inspired open set learning for retinal anomaly identification.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for diagnosing exudative age-related macular degeneration.The Cochrane database of systematic reviews · 2024Pooled it
- Boosting identification of microsporidian spores originating from different hosts: single-cell Raman spectroscopy combined with self-attention mechanism-driven convolutional neural network.Analytical and bioanalytical chemistry · 2026Article
- Uncertainty-guided model learning for trustworthy medical image segmentation.Medical & biological engineering & computing · 2026Article
- A comparison of GPT-4V's capability in optical coherence tomography images of age-related macular degeneration with expert assessments.BMC ophthalmology · 2026Article
- A test-time clinically adaptive framework for detecting multiple fundus diseases harnessing ophthalmic foundation models.NPJ digital medicine · 2026Article
- Validated semi-supervised early and accurate screening for anterior segment diseases: a 3PM-guided conceptual and technological innovation.The EPMA journal · 2026Article
- A deep learning based automatic report generator for retinal optical coherence tomography images.NPJ digital medicine · 2025Article
- A multimodal uncertainty-aware AI system optimizes ovarian cancer risk assessment workflow.NPJ digital medicine · 2025Article
- A foundational triage system for improving accuracy in moderate acuity level emergency classifications.Communications medicine · 2025Article
- Enhancing diagnostic accuracy in rare and common fundus diseases with a knowledge-rich vision-language model.Nature communications · 2025Article
- Robust Uncertainty-Informed Glaucoma Classification Under Data Shift.Translational vision science & technology · 2025Article
- Self-supervised based clustering for retinal optical coherence tomography images.Eye (London, England) · 2025Article
- Enhancing AI reliability: A foundation model with uncertainty estimation for optical coherence tomography-based retinal disease diagnosis.Cell reports. Medicine · 2025Article
- Open-set deep learning-enabled single-cell Raman spectroscopy for rapid identification of airborne pathogens in real-world environments.Science advances · 2025Article
- Efficiency and safety of automated label cleaning on multimodal retinal images.NPJ digital medicine · 2025Article
- Multi-risk factors joint prediction model for risk prediction of retinopathy of prematurity.The EPMA journal · 2024Article
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Authors and funding
24 authors.
Funding
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Abstract
Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies. We establish an uncertainty-inspired open set (UIOS) model, which is trained with fundus images of 9 retinal conditions. Besides assessing the probability of each category, UIOS also calculates an uncertainty score to express its confidence. Our UIOS model with thresholding strategy achieves an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set, external target categories (TC)-JSIEC dataset and TC-unseen testing set, respectively, compared to the F1 score of 92.20%, 80.69% and 64.74% by the standard AI model. Furthermore, UIOS correctly predicts high uncertainty scores, which would prompt the need for a manual check in the datasets of non-target categories retinal diseases, low-quality fundus images, and non-fundus images. UIOS provides a robust method for real-world screening of retinal anomalies.
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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.