CNN-LSTM Based Multimodal MRI and Clinical Data Fusion for Predicting Functional Outcome in Stroke Patients
Author:
Affiliation:
1. CREATIS, Université Lyon1, CNRS UMR5220, IN-SERM U1206, INSA-Lyon,Villeurbanne,France,69621
2. Hospices Civils de Lyon,Department of Vascular Neurology,France
3. LARIS, UMR IRHS INRA, Universite d'Angers,France
Funder
French National Research Agency (ANR)
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9870821/9870822/09871735.pdf?arnumber=9871735
Reference24 articles.
1. Multicomponent and Longitudinal Imaging Seen as a Communication Channel—An Application to Stroke
2. Local spatio-temporal encoding of raw perfusion MRI for the prediction of final lesion in stroke
3. Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text
4. Prediction of Pseudoprogression versus Progression using Machine Learning Algorithm in Glioblastoma
5. Lung cancer histology classification from CT images based on radiomics and deep learning models
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