Combining unsupervised and supervised learning for predicting the final stroke lesion

@article{Pinto2020CombiningUA,
  title={Combining unsupervised and supervised learning for predicting the final stroke lesion},
  author={Adriano Pinto and S{\'e}rgio Pereira and Raphael Meier and Roland Wiest and Victor Alves and Mauricio Reyes and Carlos A. Silva},
  journal={Medical image analysis},
  year={2020},
  volume={69},
  pages={
          101888
        }
}
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8 Citations
Highly Influential Citations
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Background Citations
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Methods Citations
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8 Citations

Prediction of Tissue Damage Using a User-Independent Machine Learning Algorithm vs. Tmax Threshold Maps

Test the accuracy of a fully automated stroke tissue estimation algorithm (FASTER) to predict final lesion volumes in an independent dataset in patients with acute stroke and found the decision forest algorithm overestimated the final infarct volume in patients without reperfusion.

Performance of Machine Learning for Tissue Outcome Prediction in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis

Machine learning (ML) has been proposed for lesion segmentation in acute ischemic stroke (AIS). This study aimed to provide a systematic review and meta-analysis of the overall performance of current…

Radiomics, machine learning, and artificial intelligence—what the neuroradiologist needs to know

Deep radiomics uses CNNs to directly extract features and obviate the need for predefined features and common limitations and pitfalls in AI-based research in neuroradiology are limited sample sizes, selection bias, as well as overfitting and underfitting.

Deep learning for neurodegenerative disorder (2016 to 2022): A systematic review

Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review

The current state-of-the-art deep learning technology in acute ischemic stroke imaging is surveyed, particularly in exploring the potential function of stroke diagnosis and multimodal prognostication.

Artificial Intelligence in Acute Ischemic Stroke

Intracerebral Haemorrhage Growth Prediction Based on Displacement Vector Field and Clinical Metadata

Title : " Deciphering astrocyte polarization in perinatal white matter injury and its role in disease pathogenesis

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