ArticleIEEE transactions on pattern analysis and machine intelligence2024
Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images.
Article in IEEE transactions on pattern analysis and machine intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Style-Semantic Disentangled Optical-to-Infrared Translation for Infrared Target Recognition.Journal of imaging · 2026Article
- AUCp: Pseudo-AUC for Inference Model Selection With Unlabeled Validation Data in Abnormality Detection.IEEE transactions on medical imaging · 2026Article
- Unsupervised Anomaly Detection in Medical Imaging: A Survey of Methods, Challenges, and Future Directions.Bioengineering (Basel, Switzerland) · 2026Article
- Dual-domain adversarial learning and feature constraint for unsupervised anomaly detection in chest X-rays.Frontiers in medicine · 2026Article
- CRTSC: Channel-Wise Recalibration and Texture-Structural Consistency Constraint for Anomaly Detection in Medical Chest Images.Sensors (Basel, Switzerland) · 2025Article
- Integrating snapshot ensemble learning into masked autoencoders for efficient self-supervised pretraining in medical imaging.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients. Exploiting this structured information could potentially ease the detection of anomalies from radiography images. To this end, we propose a Simple Space-Aware Memory Matrix for In-painting and Detecting anomalies from radiography images (abbreviated as SimSID). We formulate anomaly detection as an image reconstruction task, consisting of a space-aware memory matrix and an in-painting block in the feature space. During the training, SimSID can taxonomize the ingrained anatomical structures into recurrent visual patterns, and in the inference, it can identify anomalies (unseen/modified visual patterns) from the test image. Our SimSID surpasses the state of the arts in unsupervised anomaly detection by +8.0%, +5.0%, and +9.9% AUC scores on ZhangLab, COVIDx, and CheXpert benchmark datasets, respectively.
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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.