Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images

Deep Residual Learning for Image Recognition

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Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection

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Deep Learning for Anomaly Detection: A Review

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f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks

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Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere

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Revisiting Self-Supervised Visual Representation Learning

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Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty

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Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning

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OCGAN: One-Class Novelty Detection Using GANs With Constrained Latent Representations

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HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy

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Patch SVDD: Patch-Level SVDD for Anomaly Detection and Segmentation

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Selective Feature Aggregation Network with Area-Boundary Constraints for Polyp Segmentation

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Attention Based Glaucoma Detection: A Large-Scale Database and CNN Model

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Attention Guided Anomaly Localization in Images

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Exploiting epistemic uncertainty of anatomy segmentation for anomaly detection in retinal OCT

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Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images

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Significance-linked connected component analysis for wavelet image coding

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A new comprehensive trait database of European and Maghreb butterflies, Papilionoidea

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Unsupervised lesion detection via image restoration with a normative prior

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Unsupervised pathology detection in medical images using conditional variational autoencoders

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Patch SVDD: Patch-level SVDD for Anomaly Detection and SegmentationPreprint

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Computer-aided diagnosis for characterization of colorectal lesions: comprehensive software that includes differentiation of serrated lesions

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Self-supervised Mean Teacher for Semi-supervised Chest X-Ray Classification

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Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy

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Towards Practical Unsupervised Anomaly Detection on Retinal Images

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One-Stage Five-Class Polyp Detection and Classification

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Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI

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Photoshopping Colonoscopy Video Frames

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Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal ImagesPreprint

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Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy

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Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning

Tian, Y., Pang, G., Chen, Y., Singh, R., Verjans, J. W. H., & Carneiro, G. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00493 · Cited by 615 · see citing works

Self-supervised learning for medical image classification: a systematic review and implementation guidelines

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Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection

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ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification

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Deep learning for rice leaf disease detection: A systematic literature review on emerging trends, methodologies and techniques

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Deep One-Class Classification via Interpolated Gaussian Descriptor

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Machine Learning Empowering Personalized Medicine: A Comprehensive Review of Medical Image Analysis Methods

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Toward Generalist Anomaly Detection via In-Context Residual Learning with Few-Shot Sample Prompts

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A multi-source information fusion model for outlier detection

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Dissecting self-supervised learning methods for surgical computer vision

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MedIAnomaly: A comparative study of anomaly detection in medical images

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Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images

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Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images

Tian, Y., Liu, F., Pang, G., Chen, Y., Liu, Y., Verjans, J. W. H., Singh, R., & Carneiro, G. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102930 · Cited by 52 · see citing works

Unsupervised Anomaly Detection in Medical Images with a Memory-Augmented Multi-level Cross-Attentional Masked Autoencoder

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Encoder-Decoder Contrast for Unsupervised Anomaly Detection in Medical Images

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Contrastive Transformer-Based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection

Tian, Y., Pang, G., Liu, F., Liu, Y., Wang, C., Chen, Y., Verjans, J. W. H., & Carneiro, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16437-8_9 · Cited by 45 · see citing works

Unsupervised anomaly detection in brain MRI: Learning abstract distribution from massive healthy brains

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<i>auto</i>SMIM: Automatic Superpixel-Based Masked Image Modeling for Skin Lesion Segmentation

Wang, Z., Lyu, J., & Tang, X. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3290700 · Cited by 35 · see citing works

One-for-All: Proposal Masked Cross-Class Anomaly Detection

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Harvard Glaucoma Detection and Progression: A Multimodal Multitask Dataset and Generalization-Reinforced Semi-Supervised Learning

Luo, Y., Shi, M., Tian, Y., Elze, T., & Wang, M. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01872 · Cited by 33 · see citing works

Unsupervised Pathology Detection: A Deep Dive Into the State of the Art

Lagogiannis, I., Meissen, F., Kaissis, G. A., & Rueckert, D. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3298093 · Cited by 32 · see citing works

Devil is in the Queries: Advancing Mask Transformers for Real-world Medical Image Segmentation and Out-of-Distribution Localization

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Anatomy-aware self-supervised learning for anomaly detection in chest radiographs

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

de Bruijne, M., Cattin, P. C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., & Essert, C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87240-3 · Cited by 28 · see citing works

Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detection

Yao, X., Li, R., Qian, Z., Wang, L., & Zhang, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73411-3_6 · Cited by 26 · see citing works

FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification

Tian, Y., Wen, C., Shi, M., Afzal, M. M., Huang, H. M., Khan, M. O., Luo, Y., Fang, Y., & Wang, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73116-7_15 · Cited by 23 · see citing works

Self-Supervised Multi-Scale Cropping and Simple Masked Attentive Predicting for Lung CT-Scan Anomaly Detection

Li, W., Liu, G.-H., Fan, H., Li, Z., & Zhang, D. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3313778 · Cited by 23 · see citing works

SWSSL: Sliding Window-Based Self-Supervised Learning for Anomaly Detection in High-Resolution Images

Dong, H., Zhang, Y., Gu, H., Konz, N., Zhang, Y., & Mazurowski, M. A. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3314318 · Cited by 22 · see citing works

Self-Supervised Learning for Industrial Image Anomaly Detection by Simulating Anomalous Samples

Pei, M.-J., Liu, N., Zhao, B., & Sun, H. (2023) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-023-00328-0 · Cited by 22 · see citing works

ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangling and Self-Prompting

Jiang, Y., Huang, Z., Zhang, R., Zhang, X., & Zhang, S. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01082 · Cited by 22 · see citing works

AMAE: Adaptation of Pre-trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays

Bozorgtabar, B., Mahapatra, D., & Thiran, J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43907-0_19 · Cited by 21 · see citing works

Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays

Cai, Y., Chen, H., Yang, X. Y., Zhou, Y., & Cheng, K.-T. T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16437-8_56 · Cited by 20 · see citing works

Task-Oriented Self-supervised Learning for Anomaly Detection in Electroencephalography

Zheng, Y., Liu, Z., Mo, R., Chen, Z., Zheng, W., & Wang, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16452-1_19 · Cited by 20 · see citing works

An effective colorectal polyp classification for histopathological images based on supervised contrastive learning

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Modality Consistency-Guided Contrastive Learning for Wearable-Based Human Activity Recognition

Guo, C., Zhang, Y., Chen, Y., Xu, C., & Wang, Z. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3379019 · Cited by 18 · see citing works

Adapting the segment anything model for multi-modal retinal anomaly detection and localization

Li, J., Chen, T., Wang, X., Zhong, Y., & Xiao, X. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102631 · Cited by 17 · see citing works

Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images

Xiang, T., Zhang, Y., Lu, Y., Yuille, A., Zhang, C., Cai, W., & Zhou, Z. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3382009 · Cited by 17 · see citing works

Many Tasks Make Light Work: Learning to Localise Medical Anomalies from Multiple Synthetic Tasks

Baugh, M., Tan, J., Müller, J. P., Dombrowski, M., Batten, J., & Kainz, B. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43907-0_16 · Cited by 15 · see citing works

Frontiers in Intelligent Colonoscopy

Ji, G.-P., Liu, J., Xu, P., Barnes, N. M., Khan, F. S., Khan, S. H., & Fan, D.-P. (2026) · Machine Intelligence Research · doi:10.1007/s11633-025-1597-6 · Cited by 13 · see citing works

Rethinking Polyp Segmentation From An Out-of-distribution Perspective

Ji, G.-P., Zhang, J., Campbell, D., Xiong, H., & Barnes, N. M. (2024) · Machine Intelligence Research · doi:10.1007/s11633-023-1472-2 · Cited by 12 · see citing works

Transformer Based Models for Unsupervised Anomaly Segmentation in Brain MR Images

Ghorbel, A., Aldahdooh, A., Albarqouni, S., & Hamidouche, W. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-33842-7_3 · Cited by 11 · see citing works

Denoising diffusion model with adversarial learning for unsupervised anomaly detection on brain MRI images

Yu, J., Oh, H., Lee, Y., & Yang, J. (2024) · Pattern Recognition Letters · doi:10.1016/j.patrec.2024.10.007 · Cited by 9 · see citing works

A Benchmark for Unsupervised Anomaly Detection in Multi-Agent Trajectories

Wiederer, J., Schmidt, J., Kreßel, U., Dietmayer, K., & Belagiannis, V. (2022) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc55140.2022.9922440 · Cited by 9 · see citing works

Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET

Hassanaly, R., Brianceau, C., Solal, M., Colliot, O., & Burgos, N. (2024) · The Journal of Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2024-b87a · Cited by 8 · see citing works

Unsupervised Anomaly Detection for Volcanic Deformation in InSAR Imagery

Popescu, R. G., Anantrasirichai, N., & Biggs, J. (2025) · Earth and Space Science · doi:10.1029/2024ea003892 · Cited by 8 · see citing works

Time-series representation learning via Time-Frequency Fusion Contrasting

Zhao, W., & Fan, L. (2024) · Frontiers Artif. Intell. · doi:10.3389/frai.2024.1414352 · Cited by 9 · see citing works

A hybrid unsupervised-weakly supervised method for video anomaly detection

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FE-CLIP: Frequency Enhanced CLIP Model for Zero-Shot Anomaly Detection and Segmentation

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Normative Prior Network for Anomaly Segmentation in Retinal OCT Images

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Saliencycut: Augmenting Plausible Anomalies for Anomaly DetectionPreprint

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Anomaly Detection in Chest X-ray Images with Adversarial Masked Autoencoder

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APAN: Anti-curriculum Pseudo-Labelling and Adversarial Noises Training for Semi-supervised Medical Image Classification

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V2TCASA: Vision to text class-agnostic state-agnostic for industrial zero-shot anomaly detection

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Contra-Discriminative GAN-based Anomaly Detection Framework for Medical Images

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G2Co: Gaze-Guided Semantic Contrastive Learning for Self-Supervised Medical Image Segmentation

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Explainable AI Based Multi-Class Pneumonia Classification and Anomaly Detection from Chest X-ray

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Rare cortical folding patterns detection based on an unsupervised deep learning approach

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Learning pathological representations in neuroimaging : Predicting psychiatric diagnosis by integrating heterogeneity constraints

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Unsupervised Anomaly Detection in Medical Imaging: A Survey of Methods, Challenges, and Future Directions

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Self-supervised multi-class pre-training for unsupervised anomaly detection and segmentation in medical imagesPreprint

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