Research Presentation Sessions: Artificial Intelligence & Machine Learning & Imaging Informatics

RPS 2105 - AI in stroke and neurovascular imaging

Lectures

1
Evaluation of automated CT workflow support in acute ischaemic stroke

Evaluation of automated CT workflow support in acute ischaemic stroke

07:00Sander Temmen, Nijmegen / NL

2
Automated classification of ischemic stroke territory on Diffusion-Weighted MRI: collaboration of human knowledge and deep learning

Automated classification of ischemic stroke territory on Diffusion-Weighted MRI: collaboration of human knowledge and deep learning

07:00Ilker Özgür Koska, Izmir / TR

3
Generalisability of academically successful algorithms in stroke lesion segmentation

Generalisability of academically successful algorithms in stroke lesion segmentation

07:00Silvia Ingala, Copenaghen / DK

4
iSPAN: improved prediction of outcomes post thrombectomy with machine learning

iSPAN: improved prediction of outcomes post thrombectomy with machine learning

07:00Brendan S Kelly, Dublin / IE

5
Predicting acute stroke occurrence based on weather systems to improve clinical resource allocation: a machine learning approach

Predicting acute stroke occurrence based on weather systems to improve clinical resource allocation: a machine learning approach

07:00Máté Elöd Maros, Mannheim / DE

6
Automated MR carotid vessel wall segmentation with sparse annotation

Automated MR carotid vessel wall segmentation with sparse annotation

07:00Feng Shi, Shanghai / CN

7
A study of plaque detection by CNN-based model on carotid CTA images

A study of plaque detection by CNN-based model on carotid CTA images

07:00Weixin Xu, Beijing / CN

8
Automated carotid artery segmentation learned from diameter annotations

Automated carotid artery segmentation learned from diameter annotations

07:00Robin Camarasa, Rotterdam / NL

9
Performance of an AI-based automated identification of intracranial haemorrhage in real clinical practice

Performance of an AI-based automated identification of intracranial haemorrhage in real clinical practice

07:00Angela Ayobi, La Ciotat / FR

10
Improving haemorrhage detection in sparse-view CTs via deep learning

Improving haemorrhage detection in sparse-view CTs via deep learning

07:00Johannes Benedikt Thalhammer, Munich / DE

11
Clinical outcome prediction in paediatric traumatic brain injuries using multiparametric artificial neural networks based on CT findings, GCS score, blood glucose, and Hb levels

Clinical outcome prediction in paediatric traumatic brain injuries using multiparametric artificial neural networks based on CT findings, GCS score, blood glucose, and Hb levels

07:00Vasantha Kumar Venugopal, New Delhi / IN

12
Moderation

Moderation

00:00Sotirios Bisdas, London / UK

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