mlsanityData to 14 Oct 2026

Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification

arXiv 2304.03981 ↗First posted 8 Apr 2023cs.LG cs.CVcited by 18

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Abstract (arXiv)

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 established an uncertainty-inspired open-set (UIOS) model, which was trained with fundus images of 9 retinal conditions. Besides assessing the probability of each category, UIOS also calculated an uncertainty score to express its confidence. Our UIOS model with thresholding strategy achieved 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 predicted 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.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 20 of its 58 references that are in the map. First posted on arXiv 8 Apr 2023.

Builds on

20 of 58 references.

References · 20

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  3. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  4. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,186
  5. 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 819
  6. 2021A Survey of Uncertainty in Deep Neural Networkscited by 680
  7. 2017Regularizing Neural Networks by Penalizing Confident Output Distributionscited by 464
  8. 2017Snapshot Ensembles: Train 1, get M for freecited by 350
  9. 2018Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networkscited by 280
  10. 2020Hyperparameter Ensembles for Robustness and Uncertainty Quantificationcited by 150
  11. 2018Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentationcited by 146
  12. 2021Trusted Multi-View Classificationcited by 94
8 more
  1. 2023A Review of Uncertainty Estimation and its Application in Medical Imagingcited by 59
  2. 2020Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Imagescited by 52
  3. 2022Semi-Supervised and Unsupervised Deep Visual Learning: A Surveycited by 52
  4. 2021Trustworthy Long-Tailed Classificationcited by 51
  5. 2022Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathologycited by 37
  6. 2022TBraTS: Trusted Brain Tumor Segmentationcited by 30
  7. 2022BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networkscited by 23
  8. 2022Reliable Joint Segmentation of Retinal Edema Lesions in OCT Imagescited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 6; 2025: 6; 2026: 6.

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  1. 2024COME: Test-time adaption by Conservatively Minimizing Entropycited by 31
  2. 2024Uncertainty-aware Medical Diagnostic Phrase Identification and Groundingcited by 13
  3. 2024Enhancing Diagnostic Accuracy in Rare and Common Fundus Diseases with a Knowledge-Rich Vision-Language Modelcited by 12
  4. 2025Towards Accurate Unified Anomaly Segmentationcited by 7
  5. 2024Confidence-aware multi-modality learning for eye disease screeningcited by 5
  6. 2025Comparing Uncertainty Measurement and Mitigation Methods for Large Language Models: A Systematic Reviewcited by 4
  7. 2025Vision-Language Model IP Protection via Prompt-based Learningcited by 3
  8. 2025UCDSC: Open Set UnCertainty aware Deep Simplex Classifier for Medical Image Datasetscited by 2
  9. 2025Clinician-Friendly Foundation Models for Ophthalmic Image Diagnostics without Fine-Tuning or Technical Barrierscited by 1
  10. 2026Uncertainty and Fairness Awareness in LLM-Based Recommendation Systemscited by 1
  11. 2024Enhancing Diagnostic Reliability of Foundation Model with Uncertainty Estimation in OCT Imagescited by 0
  12. 2024Enhancing Community Vision Screening -- AI Driven Retinal Photography for Early Disease Detection and Patient Trustcited by 0
6 more
  1. 2025Weakly Supervised Pixel-Level Annotation with Visual Interpretabilitycited by 0
  2. 2026Authorize-on-Demand: Dynamic Authorization with Legality-Aware Intellectual Property Protection for VLMscited by 0
  3. 2026Open-Set Supervised 3D Anomaly Detection: An Industrial Dataset and a Generalisable Framework for Unknown Defectscited by 0
  4. 2026Are Independently Estimated View Uncertainties Comparable? Unified Routing for Trusted Multi-View Classificationcited by 0
  5. 2026MTL-MAD: Multi-Task Learners are Effective Medical Anomaly Detectorscited by 0
  6. 2026Beyond the Simplex: Balanced Prototype Geometry for Scorer-Agnostic Open-Set Recognitioncited by 0