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Machine Learning in Stroke Imaging
The objective of this project is to develop and implement various machine learning methods to the recognition, segmentation, and diagnosis of brain strokes, using a large database of computer tomographic (CT) and magnetic resonance (MR) images.
An ischemic stroke is a sudden loss of neurological function due to brain parenchyma damage caused by the sudden loss or significant decrease of blood flow to a specific region of the brain. Stroke is the second most common cause of death worldwide, the leading cause of acquired disability in adults, and the third cause of loss of years of life.
An ischemic stroke is a sudden loss of neurological function due to brain parenchyma damage caused by the sudden loss or significant decrease of blood flow to a specific region of the brain. Stroke is the second most common cause of death worldwide, the leading cause of acquired disability in adults, and the third cause of loss of years of life.
The objective of this project is to develop and implement various machine learning methods, such as deep neural network, convolutional neural network, generative adversarial network (GAN) and variational autoencoders, to the recognition, segmentation, and diagnosis of brain strokes, using a large database of computer tomographic (CT) and magnetic resonance (MR) images.
Programming experience in Python, C/C++ or equivalent is required.
The objective of this project is to develop and implement various machine learning methods, such as deep neural network, convolutional neural network, generative adversarial network (GAN) and variational autoencoders, to the recognition, segmentation, and diagnosis of brain strokes, using a large database of computer tomographic (CT) and magnetic resonance (MR) images.
Programming experience in Python, C/C++ or equivalent is required.
Dr. Christian Federau, federau@biomed.ee.ethz.ch)
Supervising Professor: Prof. Dr. Sebastian Kozerke (kozerke@biomed.ee.ethz.ch)
Dr. Christian Federau, federau@biomed.ee.ethz.ch) Supervising Professor: Prof. Dr. Sebastian Kozerke (kozerke@biomed.ee.ethz.ch)