Identifying risk factors of intracerebral hemorrhage stability using explainable attention model

Segmentation of intracerebral hemorrhage (ICH) helps improve the quality of diagnosis, draft the desired treatment methods, and clinically observe the variations with healthy patients. The clinical utilization of various ICH progression scoring systems has limitations due to the systems’ modest pred...

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Bibliographic Details
Published in:	Medical & biological engineering & computing Vol. 60; no. 2; pp. 337 - 348
Main Authors:	Rangaraj, Seshasayi, Islam, Mobarakol, VS, Vibashan, Wijethilake, Navodini, Uppal, Utkarsh, See, Angela An Qi, Chan, Jasmine, James, Michael Lucas, King, Nicolas Kon Kam, Ren, Hongliang
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01-02-2022 Springer Nature B.V
Subjects:	Accuracy Angiography Attention Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Cerebral Hemorrhage - diagnostic imaging Computed Tomography Angiography Computer Applications Feature extraction Glasgow Coma Scale Hemorrhage Human Physiology Humans Imaging Learning algorithms Machine learning Multilayer perceptrons Original Article Patients Radiology Risk analysis Risk Factors Segmentation Stability Stroke Support vector machines 3D CNN Risk prediction Attention network Growth rate estimation Segmentation Intracerebral hemorrhage
Online Access:	Get full text
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