ArticleScientific reports2021
An anomaly detection approach to identify chronic brain infarcts on MRI.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review.Frontiers in public health · 2025Pooled it
- Efficient slice anomaly detection network for 3D brain MRI Volume.PLOS digital health · 2025Article
- Diagnostic classification in toxicologic pathology using attention-guided weak supervision and whole slide image features: a pilot study in rat livers.Scientific reports · 2025Article
- Anomaly Detection in High-Dimensional Time Series Data with Scaled Bregman Divergence.Algorithms · 2025Article
- VISION: View-specific integrated segmentation-classification framework for accurate brain tumor detection in MRI scans.PloS one · 2025Article
- Quantifying impairment and disease severity using AI models trained on healthy subjects.NPJ digital medicine · 2024Article
- Anomaly Detection in Optical Coherence Tomography Angiography (OCTA) with a Vector-Quantized Variational Auto-Encoder (VQ-VAE).Bioengineering (Basel, Switzerland) · 2024Article
- Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals.Korean journal of radiology · 2024Review
- Overcoming the Challenges in the Development and Implementation of Artificial Intelligence in Radiology: A Comprehensive Review of Solutions Beyond Supervised Learning.Korean journal of radiology · 2023Review
- Unsupervised abnormality detection in neonatal MRI brain scans using deep learning.Scientific reports · 2023Article
- Data-Driven Quantitation of Movement Abnormality after Stroke.Bioengineering (Basel, Switzerland) · 2023Article
- A Study on the Effectiveness of Deep Learning-Based Anomaly Detection Methods for Breast Ultrasonography.Sensors (Basel, Switzerland) · 2023Article
- Segmentation of incident lacunes during the course of ischemic cerebral small vessel diseases.Frontiers in neurology · 2023Article
- Off-targetP ML: an open source machine learning framework for off-target panel safety assessment of small molecules.Journal of cheminformatics · 2022Article
- Detecting Unusual Intravenous Infusion Alerting Patterns with Machine Learning Algorithms.Biomedical instrumentation & technology · 2022Article
- Outlier detection in multimodal MRI identifies rare individual phenotypes among more than 15,000 brains.Human brain mapping · 2022Article
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Authors and funding
6 authors at 1 institution in 2 countries.
Funding
Abstract
The performance of current machine learning methods to detect heterogeneous pathology is limited by the quantity and quality of pathology in medical images. A possible solution is anomaly detection; an approach that can detect all abnormalities by learning how 'normal' tissue looks like. In this work, we propose an anomaly detection method using a neural network architecture for the detection of chronic brain infarcts on brain MR images. The neural network was trained to learn the visual appearance of normal appearing brains of 697 patients. We evaluated its performance on the detection of chronic brain infarcts in 225 patients, which were previously labeled. Our proposed method detected 374 chronic brain infarcts (68% of the total amount of brain infarcts) which represented 97.5% of the total infarct volume. Additionally, 26 new brain infarcts were identified that were originally missed by the radiologist during radiological reading. Our proposed method also detected white matter hyperintensities, anomalous calcifications, and imaging artefacts. This work shows that anomaly detection is a powerful approach for the detection of multiple brain abnormalities, and can potentially be used to improve the radiological workflow efficiency by guiding radiologists to brain anomalies which otherwise remain unnoticed.
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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.