# Dual-Mode Deep Anomaly Detection for Medical Manufacturing: Structural Similarity and Feature Distance

arXiv: https://arxiv.org/abs/2509.05796 · HTML: https://mlsanity.com/paper/2509.05796/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 51 references that are in the map.
- First posted on arXiv 6 Sep 2025.

## Builds on (most cited first, 8 of 8)

- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization](https://mlsanity.com/paper/2011.08785/) (2020)
- [One-Shot Learning for Semantic Segmentation](https://mlsanity.com/paper/1709.03410/) (2017)
- [LIFT: Learned Invariant Feature Transform](https://mlsanity.com/paper/1603.09114/) (2016)
- [Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?](https://mlsanity.com/paper/1706.00712/) (2017)
- [Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection](https://mlsanity.com/paper/2103.15158/) (2021)
- [MOCCA: Multi-Layer One-Class ClassificAtion for Anomaly Detection](https://mlsanity.com/paper/2012.12111/) (2020)
- [Deep Variational Semi-Supervised Novelty Detection](https://mlsanity.com/paper/1911.04971/) (2019)

## Abstract (arXiv)

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.