mlsanityData to 7 Oct 2026

Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions

arXiv 2104.03829 ↗First posted 8 Apr 2021cs.CV cs.LGcited by 68

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Abstract (arXiv)

We develop and rigorously evaluate a deep learning based system that can accurately classify skin conditions while detecting rare conditions for which there is not enough data available for training a confident classifier. We frame this task as an out-of-distribution (OOD) detection problem. Our novel approach, hierarchical outlier detection (HOD) assigns multiple abstention classes for each training outlier class and jointly performs a coarse classification of inliers vs. outliers, along with fine-grained classification of the individual classes. We demonstrate the effectiveness of the HOD loss in conjunction with modern representation learning approaches (BiT, SimCLR, MICLe) and explore different ensembling strategies for further improving the results. We perform an extensive subgroup analysis over conditions of varying risk levels and different skin types to investigate how the OOD detection performance changes over each subgroup and demonstrate the gains of our framework in comparison to baselines. Finally, we introduce a cost metric to approximate downstream clinical impact. We use this cost metric to compare the proposed method against a baseline system, thereby making a stronger case for the overall system effectiveness in a real-world deployment scenario.

Key facts. Cited by 68 papers in the map (data through 7 Oct 2026). Builds on 39 of its 57 references that are in the map. First posted on arXiv 8 Apr 2021.

Builds on

39 of 57 references.

References · 39

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  3. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  4. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  5. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  6. 2020The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalizationcited by 1,842
  7. 2018Group Normalizationcited by 1,514
  8. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,472
  9. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,325
  10. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,274
  11. 2018Deep Anomaly Detection with Outlier Exposurecited by 939
  12. 2017Revisiting Unreasonable Effectiveness of Data in Deep Learning Eracited by 916
27 more
  1. 2019AugMix: A Simple Data Processing Method to Improve Robustness and Uncertaintycited by 897
  2. 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 774
  3. 2019Big Transfer (BiT): General Visual Representation Learningcited by 766
  4. 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 451
  5. 2019Deep Ensembles: A Loss Landscape Perspectivecited by 438
  6. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 423
  7. 2019Using Pre-Training Can Improve Model Robustness and Uncertaintycited by 373
  8. 2021Big Self-Supervised Models Advance Medical Image Classificationcited by 320
  9. 2020Pretrained Transformers Improve Out-of-Distribution Robustnesscited by 293
  10. 2020A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promisescited by 230
  11. 2020Contrastive Training for Improved Out-of-Distribution Detectioncited by 186
  12. 2020Hyperparameter Ensembles for Robustness and Uncertainty Quantificationcited by 149
  13. 2019A deep learning system for differential diagnosis of skin diseasescited by 118
  14. 2020Hybrid Models for Open Set Recognitioncited by 109
  15. 2020Density of States Estimation for Out-of-Distribution Detectioncited by 74
  16. 2017Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentationcited by 58
  17. 2019Micro-Batch Training with Batch-Channel Normalization and Weight Standardizationcited by 45
  18. 2020A Benchmark of Medical Out of Distribution Detectioncited by 40
  19. 2021Supervised Transfer Learning at Scale for Medical Imagingcited by 38
  20. 2015Universum Prescription: Regularization using Unlabeled Datacited by 16
  21. 2020Self-Supervised Out-of-Distribution Detection in Brain CT Scanscited by 10
  22. 2020Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Modelscited by 9
  23. 2018Prototypical Clustering Networks for Dermatological Disease Diagnosiscited by 6
  24. 2020Out-of-Distribution Detection for Skin Lesion Images with Deep Isolation Forestcited by 6
  25. 2019FRODO: Free rejection of out-of-distribution samples: application to chest x-ray analysiscited by 5
  26. 2020Addressing the Real-world Class Imbalance Problem in Dermatologycited by 5
  27. 2021CheXseen: Unseen Disease Detection for Deep Learning Interpretation of Chest X-rayscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 10; 2022: 19; 2023: 23; 2024: 9; 2025: 3; 2026: 4.

68 papers.

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  1. 2021Exploring the Limits of Out-of-Distribution Detectioncited by 283
  2. 2022Delving into Out-of-Distribution Detection with Vision-Language Representationscited by 215
  3. 2021DICE: Leveraging Sparsification for Out-of-Distribution Detectioncited by 166
  4. 2021Learning Optimal Conformal Classifierscited by 108
  5. 2021On the Impact of Spurious Correlation for Out-of-distribution Detectioncited by 68
  6. 2021No One Representation to Rule Them All: Overlapping Features of Training Methodscited by 57
  7. 2022A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awarenesscited by 49
  8. 2022Diagnosing failures of fairness transfer across distribution shift in real-world medical settingscited by 45
  9. 2022Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognitioncited by 44
  10. 2022Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithmcited by 35
  11. 2021Confidence-based Out-of-Distribution Detection: A Comparative Study and Analysiscited by 34
  12. 2021Dangers of Bayesian Model Averaging under Covariate Shiftcited by 31
38 more
  1. 2023Conformal prediction under ambiguous ground truthcited by 31
  2. 2022Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labelscited by 30
  3. 2024Out-of-distribution Detection in Medical Image Analysis: A surveycited by 22
  4. 2021Reliable and Trustworthy Machine Learning for Health Using Dataset Shift Detectioncited by 21
  5. 2024Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directionscited by 17
  6. 2022Adversarial vulnerability of powerful near out-of-distribution detectioncited by 16
  7. 2023Devil is in the Queries: Advancing Mask Transformers for Real-world Medical Image Segmentation and Out-of-Distribution Localizationcited by 16
  8. 2023EAT: Towards Long-Tailed Out-of-Distribution Detectioncited by 16
  9. 2023On the use of Mahalanobis distance for out-of-distribution detection with neural networks for medical imagingcited by 15
  10. 2023Out-of-Distribution Detection by Leveraging Between-Layer Transformation Smoothnesscited by 15
  11. 2023ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangling and Self-Promptingcited by 15
  12. 2023AUTO: Adaptive Outlier Optimization for Test-Time OOD Detectioncited by 12
  13. 2023Modality Cycles with Masked Conditional Diffusion for Unsupervised Anomaly Segmentation in MRIcited by 12
  14. 2022Out-of-Distribution Detection for Long-tailed and Fine-grained Skin Lesion Imagescited by 10
  15. 2022A Reduction to Binary Approach for Debiasing Multiclass Datasetscited by 9
  16. 2022Recommendations on test datasets for evaluating AI solutions in pathologycited by 8
  17. 2024AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnosticscited by 8
  18. 2021Exploring Covariate and Concept Shift for Detection and Calibration of Out-of-Distribution Datacited by 7
  19. 2022Raising the Bar on the Evaluation of Out-of-Distribution Detectioncited by 7
  20. 2023Out-of-distribution detection algorithms for robust insect classificationcited by 7
  21. 2023Beyond AUROC & co. for evaluating out-of-distribution detection performancecited by 7
  22. 2023Evaluating AI systems under uncertain ground truth: a case study in dermatologycited by 7
  23. 2023LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentationcited by 7
  24. 2022Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directionscited by 5
  25. 2022A Comprehensive Review of Trends, Applications and Challenges In Out-of-Distribution Detectioncited by 5
  26. 2023SmoothHess: ReLU Network Feature Interactions via Stein's Lemmacited by 5
  27. 2023Morse Neural Networks for Uncertainty Quantificationcited by 4
  28. 2023Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularizationcited by 4
  29. 2025Systematic Evaluation of Large Vision-Language Models for Surgical Artificial Intelligencecited by 4
  30. 2025WeedNet: A Foundation Model-Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classificationcited by 4
  31. 2023Set Learning for Accurate and Calibrated Modelscited by 2
  32. 2024MINT: A wrapper to make multi-modal and multi-image AI models interactivecited by 2
  33. 2024FlowCon: Out-of-Distribution Detection using Flow-Based Contrastive Learningcited by 2
  34. 2024MONICA: Benchmarking on Long-tailed Medical Image Classificationcited by 2
  35. 2022Mixture of basis for interpretable continual learning with distribution shiftscited by 1
  36. 2022Deep Dirichlet uncertainty for unsupervised out-of-distribution detection of eye fundus photographs in glaucoma screeningcited by 1
  37. 2022Quantification of Robotic Surgeries with Vision-Based Deep Learningcited by 1
  38. 2022Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectorscited by 1

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