Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks
Chinese University of Hong Kong · Shenzhen University
Abstract
Cerebral microbleeds (CMBs) are small haemorrhages nearby blood vessels. They have been recognized as important diagnostic biomarkers for many cerebrovascular diseases and cognitive dysfunctions. In current clinical routine, CMBs are manually labelled by radiologists but this procedure is laborious, time-consuming, and error prone. In this paper, we propose a novel automatic method to detect CMBs from magnetic resonance (MR) images by exploiting the 3D convolutional neural network (CNN). Compared with previous methods that employed either low-level hand-crafted descriptors or 2D CNNs, our method can take full advantage of spatial contextual information in MR volumes to extract more representative high-level…
Citation impact
- FWCI
- 61.92
- Percentile
- 100%
- References
- 64
Authors
9Topics & keywords
- Convolutional neural network
- Computer science
- Artificial intelligence
- False positive paradox
- Pattern recognition (psychology)
- Margin (machine learning)
- Sensitivity (control systems)
- Machine learning
- Peace, Justice and strong institutions