Dual-Mode Deep Anomaly Detection for Medical Manufacturing: Structural Similarity and Feature Distance
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915614499536896 |
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| author | Diaz, Julio Zanon Siogkas, Georgios Corcoran, Peter |
| author_facet | Diaz, Julio Zanon Siogkas, Georgios Corcoran, Peter |
| contents | Automated visual inspection in medical-device manufacturing faces unique challenges, including extremely low defect rates, limited annotated data, hardware restrictions on production lines, and the need for validated, explainable artificial-intelligence systems. This paper presents two attention-guided autoencoder architectures that address these constraints through complementary anomaly-detection strategies. The first employs a multi-scale structural-similarity (4-MS-SSIM) index for inline inspection, enabling interpretable, real-time defect detection on constrained hardware. The second applies a Mahalanobis-distance analysis of randomly reduced latent features for efficient feature-space monitoring and lifecycle verification. Both approaches share a lightweight backbone optimised for high-resolution imagery for typical manufacturing conditions. Evaluations on the Surface Seal Image (SSI) dataset-representing sterile-barrier packaging inspection-demonstrate that the proposed methods outperform reference baselines, including MOCCA, CPCAE, and RAG-PaDiM, under realistic industrial constraints. Cross-domain validation on the MVTec-Zipper benchmark confirms comparable accuracy to state-of-the-art anomaly-detection methods. The dual-mode framework integrates inline anomaly detection and supervisory monitoring, advancing explainable AI architectures toward greater reliability, observability, and lifecycle monitoring in safety-critical manufacturing environments. To facilitate reproducibility, the source code developed for the experiments has been released in the project repository, while the datasets were obtained from publicly available sources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05796 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dual-Mode Deep Anomaly Detection for Medical Manufacturing: Structural Similarity and Feature Distance Diaz, Julio Zanon Siogkas, Georgios Corcoran, Peter Computer Vision and Pattern Recognition Artificial Intelligence Automated visual inspection in medical-device manufacturing faces unique challenges, including extremely low defect rates, limited annotated data, hardware restrictions on production lines, and the need for validated, explainable artificial-intelligence systems. This paper presents two attention-guided autoencoder architectures that address these constraints through complementary anomaly-detection strategies. The first employs a multi-scale structural-similarity (4-MS-SSIM) index for inline inspection, enabling interpretable, real-time defect detection on constrained hardware. The second applies a Mahalanobis-distance analysis of randomly reduced latent features for efficient feature-space monitoring and lifecycle verification. Both approaches share a lightweight backbone optimised for high-resolution imagery for typical manufacturing conditions. Evaluations on the Surface Seal Image (SSI) dataset-representing sterile-barrier packaging inspection-demonstrate that the proposed methods outperform reference baselines, including MOCCA, CPCAE, and RAG-PaDiM, under realistic industrial constraints. Cross-domain validation on the MVTec-Zipper benchmark confirms comparable accuracy to state-of-the-art anomaly-detection methods. The dual-mode framework integrates inline anomaly detection and supervisory monitoring, advancing explainable AI architectures toward greater reliability, observability, and lifecycle monitoring in safety-critical manufacturing environments. To facilitate reproducibility, the source code developed for the experiments has been released in the project repository, while the datasets were obtained from publicly available sources. |
| title | Dual-Mode Deep Anomaly Detection for Medical Manufacturing: Structural Similarity and Feature Distance |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2509.05796 |