Simple statistical methods for unsupervised brain anomaly detection on MRI are competitive to deep learning methods. Statistical analysis of magnetic resonance imaging (MRI) can help radiologists to detect pathologies that are otherwise likely to be missed.
Nov 25, 2020
Here, we show that also simple statistical methods such as voxel-wise (baseline and covariance) models and a linear projection method using spatial patterns can ...
Statistical analysis of magnetic resonance imaging (MRI) can help radiologists to detect pathologies that are otherwise likely to be missed. Deep learning ...
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What is unsupervised anomaly detection in brain MRI?
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Statistical analysis of magnetic resonance imaging (MRI) can help radiologists to detect pathologies that are otherwise likely to be missed. Deep learning ...
Unsupervised anomaly detection in brain MRI - ScienceDirect.com
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To develop a general unsupervised anomaly detection method based only on MR images of normal brains to automatically detect various brain abnormalities.
Missing: Simple competitive
Simple statistical methods for unsupervised brain anomaly detection on MRI are competitive to deep learning methods. CoRR abs/2011.12735 (2020). [+] ...
Deep unsupervised representation learning has recently led to new approaches in the field of Unsupervised Anomaly Detection (UAD) in brain MRI.
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6 Excerpts. Simple statistical methods for unsupervised brain anomaly detection on MRI are competitive to deep learning methods · Victor SaaseH. WenzT ...
The proposed approach achieved better accuracy compared to standard techniques, with a remarkable 99.5% accuracy in our analysis.
Jul 17, 2024 · Deep learning (DL) methods show promise in tasks like the segmentation of brain pathologies in magnetic resonance imaging (MRI) scans [19] .