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

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Unpaired image-to-image translation using cycle-consistent adversarial networks

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Image-to-Image Translation with Conditional Adversarial Networks

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GAN（Generative Adversarial Nets）

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A survey on deep learning in medical image analysis

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Deep learning in medical image analysis

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Ridge-based vessel segmentation in color images of the retina

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Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

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Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response

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MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection

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Weakly supervised anomaly detection based on sparsity prior

Wang, K., Zhang, S.-X., Cao, Y., & Yang, L. (2024) · Electronic Research Archive · doi:10.3934/era.2024169 · Cited by 3

A Norm-Constrained Method for Semantic and Structured Anomaly Detection Tasks

Gao, H., Luo, H., Shen, F., & Zhang, Z. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3427758 · Cited by 4

MLAD: Manifest and latent anomaly detection based on the integration of reconstruction and MLFP-KNN methods

Shao, P., & Lu, T. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad98b4 · Cited by 4

Y-GAN: Learning Dual Data Representations for Efficient Anomaly DetectionPreprint

Ivanovska, M., & Štruc, V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.14020 · Cited by 3

PADNet: Progressive-Difference-Aware Feature Reconstruction Mechanism for Anomaly Detection

Yang, F., Jing, P., Wang, W., Wang, F. L., & Su, Y. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3613127 · Cited by 4

A Deep Learning-Based Animation Video Image Data Anomaly Detection and Recognition Algorithm

Cheng, L., & Qian, Q. (2024) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.345929 · Cited by 2

Skip-CBAM Based Unsupervised Anomaly Detection of Fused Magnesia Furnace

Wang, Y., & Liu, Q. (2024) · International Conference on Industrial Artificial Intelligence (IAI) · doi:10.1109/iai63275.2024.10730244 · Cited by 2

Re-synthesis Anomaly Detection Framework for Driving Scenes Guided by Uncertain Metrics

Yan, S., Chen, L., Wang, C., Liu, Y., Zhai, P., & Zhang, L. (2022) · International Conference on Computing and Artificial Intelligence (ICCAI) · doi:10.1145/3532213.3532301 · Cited by 1

PGTNet: Prototype Guided Transfer Network for Few-Shot Anomaly Localization

Zhuang, Z., Zhang, J., Xiao, L., Ma, T., & He, L. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897566 · Cited by 1

The study of retinal vessel segmentation based on improved U-net algorithm

Shen, T., & Dumlao, M. F. (2022) · IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) · doi:10.1109/iaeac54830.2022.9930007 · Cited by 1

Dual-Teacher Network with SSIM Based Reverse Distillation for Anomaly Detection

Li, W., Huang, R., & Wang, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8493-6_19 · Cited by 1

Zero-Shot Eggshell Crack Detection Using Grounding DINO and FFT-Based Outer-to-Inner Ring Energy Ratio

Soontornnapar, T., & Ploysuwan, T. (2025) · IEEE Access · doi:10.1109/access.2025.3571984 · Cited by 4

PAttL-GAN: Pixel Attention Localization Generative Adversarial Network for unsupervised anomaly detection in medical images

Li, G., Zou, Y., Liu, B., Jiang, N., Lv, J., Ai, W., Ji, D., Zhou, Q., & Zhang, H. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131345 · Cited by 1

Hybrid Dual-Heterogeneous Knowledge Distillation Network for Anomaly Detection in Retinal OCT Images

Xu, M., Wei, H., Nie, Z., Zhou, X., Fu, B., Yan, H., Wan, Y., & Song, W. (2026) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3650072 · Cited by 1

BPGG: Bidirectional Prototype Generation and Guidance Network for Few-Shot Anomaly Localization

Zhang, J., Zhuang, Z., Xu, J., Ma, T., & He, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15934-3_3 · Cited by 1

Image Anomaly Detection With Semantic- Enhanced Augmentation and Distributional Kernel

Wang, M., Zhu, Y., Li, G., Liu, G., & Yang, B. (2022) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-dss-smartcity-dependsys57074.2022.00054 · Cited by 1

Out-of-Distribution with Text-to-Image Diffusion Models

Tong, J., & Dai, L. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8552-4_22 · Cited by 1

Unsupervised anomaly detection via dual transformation‐aware embeddings

Wang, Z., Hou, C., Ge, B., Liu, Y., Dong, Z., & Wu, Z. (2022) · IET Image Processing · doi:10.1049/ipr2.12438 · Cited by 1

Artificial Intelligence Cad Systems for the Detection and Classification of Retinal Diseases from Oct Images: A ReviewPreprint

Haddad, Z., Désiré Sidibé, A., Tabia, H., Zgolli, H., & Khlifa, N. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5122559 · Cited by 1

Image Quality Assessment Driven Self-Supervised Anomaly Detection

Wang, Z., Hou, C., Liu, Y., Ge, B., Yue, G., & Song, C. (2021) · IEEE International Conference on Multimedia & Expo Workshops (ICMEW) · doi:10.1109/icmew53276.2021.9455946

BIIR: Blind Inpainting based Image Reconstructon for Texture Defect Detection

Qiu, Y., Zhou, C., Zhang, Z., & Li, B. (2021) · International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE) · doi:10.1109/icbase53849.2021.00108

AutoAno: Anomaly Localization with Self-supervised Multi-scale Feature and Multivariate Gaussian Estimation

Tong, Q., Sun, M., Wang, B., & Liu, D. (2021) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1145/3498851.3498981

Pixel-Level Feature Clustering Learning for Image Anomaly Detection and Localization

Chao, H. (2022) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip56608.2022.10016612

Mtddpm: A Novel Visual Anomaly Detection Method Based on Multiscale Denoising Diffusion Probabilistic ModelPreprint

Wang, X., & Li, W. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4511134

Saliencycut: Augmenting Plausible Anomalies for Anomaly DetectionPreprint

Ye, J., Hu, Y., Yang, X., Wang, Q., Huang, C., & Huang, K. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4634960

Many-to-One: Transformer-based unsupervised anomaly detection and localization on industrial images

Dodda, N. J., & Kobti, Z. (2023) · International Conference on Machine Learning and Applications · doi:10.1109/icmla58977.2023.00184

Towards Visually Interpreting Variational AutoencodersPreprint

Li, R., Liu, W., Zheng, M., Torop, M., Rajadhyaksha, M. M., Dy, J., Köse, K., Karanam, S., Wu, Z., Bhanu, B., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-4331222/v1

Orthogonal Latent Compression for Streaming Anomaly Detection in Industrial Vision

Gao, H., Luo, H., Shen, F., & Zhang, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78189-6_7

IRAD: Input-Reference Joint Driven Reconstruction for Unified Anomaly Detection

Chen, Z., Yao, X., Luo, Y., Liu, Z., Zhang, B., & Zhang, C. (2024) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip63160.2024.10849884

Detecting Retinal Diseases Through the Implementation of Deep Convolutional Neural Networks

Saranya, S., & Ajay, K. (2024) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces63552.2024.10859983

Composite Awareness-Based Knowledge Distillation for Medical Anomaly Detection

Dong, S., & Ding, X. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-3349-4_18

Self-Supervised Metric Learning for Gaussian Anomaly Detection in Fundus Images

Langrognet, T., Faucon, T., & Decencière, É. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10981124

Deep Embedded Auto-encoder for End-to-End Unsupervised Image Anomaly Detection

Huang, X., & Tang, H. (2025) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-025-00860-1

Multi-Scale U-Shape Transformer Network for Unsupervised Image Anomaly Detection

Zou, Q., Kang, C., & Liu, Q. (2025) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd64889.2025.11033240

TS-AC: Teacher–Student Model With Aggregation Masked Convolution and Concentrated Linear Attention for Chest CT Abnormality Detection

Liu, D., Wang, H., & Zhang, F. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3600720

FC2P: Feature Cross-Channel Projection for Unsupervised Anomaly Segmentation

Chen, Y., Xian, W., Wang, J., Tao, X., & Chen, B. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3608319

DDMF: a PCB surface defect detection model based on conditional denoising diffusion and multiscale feature fusion

Deng, W., Yan, L., & Wang, C. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07853-z

Conditional Diffusion Models for Guided Anomaly Detection in Brain MRI Using Fluid-Driven Anomaly Randomization

Aguila, A. L., Liu, P., Puonti, O., & Iglesias, J. E. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05472-2_7

Anomaly Detection in Anterior Eye Segment Using Self-supervised Siamese Autoencoders

Baumstimler, P., Gagné, S., Champagne, M., Gagnon, J.-M., Playout, C., & Séoud, L. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-10351-2_5

Vda: A vision-based network for open-set defect detection with multi-level feature fusion

Jiang, Q., Xu, K., & Wang, N. (2026) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02928-y

Semantic Augmentation Variational Autoencoder for Unsupervised Anomaly Detection in Retinal OCT Images

Zhou, X., Niu, S., Han, X., Li, X., Gao, X., Feng, G., & Shi, J. (2026) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2026.3667146

Deep learning based anomaly and drift detection : application to predictive maintenance in embedded systems

Mayaki, M. Z. A. (2024) · theses.fr (ABES) · doi:10.70675/e8e2614az79ffz4901z9c79ze9f9889ba9c4

Semi-supervised deep learning for robust anomaly detection in smart manufacturing systems

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A swin transformer enhanced reverse knowledge distillation model for industrial anomaly detection via window-aware stochastic local perturbation

Peng, Y., & Qin, Y. (2026) · Discover Applied Sciences · doi:10.1007/s42452-026-08699-7

A Method for Anomaly Detection of Train Components Based on Feature Reconstruction

Yang, Z., Xiaorong, G., Luo, L., & Jianqiang, G. (2025) · IEEE Far East NDT New Technology & Application Forum (FENDT) · doi:10.1109/fendt66689.2025.11547363

Structural–Textural Distillation With Frequency‐Aware Prototype Disentanglement for Image Anomaly Detection

Zeng, Y., Yan, J., Zhang, Y., Peng, W., Li, Q., & Yi, G. (2026) · IET Image Processing · doi:10.1049/ipr2.70449

MedSAD-Clip: Supervised Clip with Token-Patch Cross-Attention for Medical Anomaly Detection and Segmentation

Tran, T. T., Do, M. K., Duy, P. N., & Lee, M. H. (2026) · doi:10.1109/cvprf71909.2026.00497

Memorizing Structure-Texture Correspondence for Image Anomaly Detection

Zhou, K. Q., Li, J., Xiao, Y., Yang, J., Cheng, J. T., Liu, W., Luo, W., Liu, J., & Gao, S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3101403 · Cited by 78

Normative Prior Network for Anomaly Segmentation in Retinal OCT Images

Zhou, X., Dong, J., Li, X., Han, T., Hou, Q., & Gao, X. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-0923-0_40

ED-AnoNet: Elastic Distortion-Based Unsupervised Network for OCT Image Anomaly Detection

Li, Y., Li, J., Shen, H., & Chen, Z. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-18910-4_1 · Cited by 4

Anomaly detection via gating highway connection for retinal fundus images

Zhang, W., Liu, H., Xie, J., Huang, Y., Zhang, Y., Li, Y., Ramachandra, R., & Zheng, Y. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.110167 · Cited by 14

A review on texture-based methods for anomaly detection in retinal optical coherence tomography images

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