ArticleJournal of digital imaging2022
Unsupervised Deep Anomaly Detection for Medical Images Using an Improved Adversarial Autoencoder.
Article in Journal of digital imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 47 citations in OpenAlex.
- Unsupervised Anomaly Detection in Medical Imaging: A Survey of Methods, Challenges, and Future Directions.Bioengineering (Basel, Switzerland) · 2026Article
- Anatomically constrained liver CT anomaly detection using healthy priors with diffusion-based inpainting.Research square · 2026Article
- Evaluation of an interpretable deep-learning model for the automated plan review of intensity-modulated radiation therapy.Frontiers in oncology · 2026Article
- Generative Inpainting-Based Anomaly Detection for CT Liver Tumor Detection.IEEE transactions on radiation and plasma medical sciences · 2025Article
- Utility of Deep Learning to Address Missing Modalities from Multi-Modal Medical Imaging Studies: A Systematic Review.Artificial intelligence and applications (Commerce, Calif.) · 2025Article
- AI-Powered Mobile App for Nuclear Cataract Detection.Sensors (Basel, Switzerland) · 2025Article
- Representation of high-dimensional cell morphology and morphodynamics in 2D latent space.Physical biology · 2025Article
- Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation.Diagnostics (Basel, Switzerland) · 2025Review
- A systematic survey: role of deep learning-based image anomaly detection in industrial inspection contexts.Frontiers in robotics and AI · 2025Review
- High-quality semi-supervised anomaly detection with generative adversarial networks.International journal of computer assisted radiology and surgery · 2024Article
- A Review of Machine Learning Algorithms for Biomedical Applications.Annals of biomedical engineering · 2024Review
- Scalable Learning Framework for Detecting New Types of Twitter Spam with Misuse and Anomaly Detection.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Anomaly detection has been applied in the various disease of medical practice, such as breast cancer, retinal, lung lesion, and skin disease. However, in real-world anomaly detection, there exist a large number of healthy samples, and but very few sick samples. To alleviate the problem of data imbalance in anomaly detection, this paper proposes an unsupervised learning method for deep anomaly detection based on an improved adversarial autoencoder, in which a module called chain of convolutional block (CCB) is employed instead of the conventional skip-connections used in adversarial autoencoder. Such CCB connections provide considerable advantages via direct connections, not only preserving both global and local information but also alleviating the problem of semantic disparity between the encoding features and the corresponding decoding features. The proposed method is thus able to capture the distribution of normal samples within both image space and latent vector space. By means of minimizing the reconstruction error within both spaces during training phase, higher reconstruction error during test phase is indicative of an anomaly. Our method is trained only on the healthy persons in order to learn the distribution of normal samples and can detect sick samples based on high deviation from the distribution of normality in an unsupervised way. Experimental results for multiple datasets from different fields demonstrate that the proposed method yields superior performance to state-of-the-art methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.