Background and activities
PhD candidate at the Department of Circulation and Medical Imaging financed by CIUS. Current research projects involve automatic methods for analysing medical images using machine learning methodology. The main application is cardiac ultrasound imaging, where the goal is to develop good diagnostic tools and improve workflow in the clinic.
Research interests
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Ultrasound, and other medical imaging modalities
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Machine learning with emphasis on deep neural networks
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Robotics and machine vision in medical intervention
Background
Master of Science (MSc) in physics and mathematics with specialization in biophysics and medical technology. Master thesis on the topic of Robot Control in Image-Guided Intervention.
Scientific, academic and artistic work
Displaying a selection of activities. See all publications in the database
Journal publications
- (2018) Ultrasound speckle reduction using generative adversial networks. Proceedings - IEEE Ultrasonics Symposium.
- (2018) Detection of Cardiac Events in Echocardiography using 3D Convolutional Recurrent Neural Networks. Proceedings - IEEE Ultrasonics Symposium.
- (2018) Deep learning applied to multi-structure segmentation in 2D echocardiography: A preliminary investigation of the required database size. Proceedings - IEEE Ultrasonics Symposium.
- (2018) Fully automatic real-time ejection fraction and MAPSE measurements in 2D echocardiography using deep neural networks. Proceedings - IEEE Ultrasonics Symposium.
- (2018) Real-time Standard View Classification in Transthoracic Echocardiography using Convolutional Neural Networks. Ultrasound in Medicine and Biology.
- (2018) Automatic Myocardial Strain Imaging in Echocardiography Using Deep Learning. Lecture Notes in Computer Science. vol. 11045.
- (2017) 2D left ventricle segmentation using deep learning. Proceedings - IEEE Ultrasonics Symposium.