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According to statistics , intracranial aneurysms (IA) are present in 3-7% of the population
According to statistics , intracranial aneurysms (IA) are present in 3-7% of the population
Deep learning models are generally more powerful than statistical learning models because deep networks can extract more inherently complex features and relationships from data
Deep learning models are generally more powerful than statistical learning models because deep networks can extract more inherently complex features and relationships from data
Recently, a study published in the European Radiology journal proposed a new deep learning model for aneurysm rupture prediction, providing technical support for early identification and diagnosis of aneurysms .
This study uses a segmented aneurysm model as input, pre-trains the model with a self-supervised approach, and learns deep embeddings of aneurysm morphology from 947 unlabeled vascular image cases
Our method achieved an area under the receiver operating characteristic curve (AUC) of 0.
The method proposed in this study allows us to develop a competitive deep learning model for predicting aneurysm rupture with limited data
Original source:
Original source:Chubin Ou, Caizi Li, Yi Qian, et al.
Chubin Ou,Caizi Li,Yi Qian,et al.
Morphology-aware multi-source fusion-based intracranial aneurysms rupture prediction .
DOI : 10.
1007/s00330-022-08608-7.
Morphology-aware multi-source fusion-based intracranial aneurysms rupture prediction 10.
1007/s00330-022-08608-7.
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