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.
Ultrasound, and other medical imaging modalities
Machine learning with emphasis on deep neural networks
Robotics and machine vision in medical intervention
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
- (2019) Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography. IEEE Transactions on Medical Imaging.
- (2018) Ultrasound speckle reduction using generative adversial networks. Proceedings - IEEE Ultrasonics Symposium. vol. 2018-October:8579764.
- (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 LNCS.
- (2017) 2D left ventricle segmentation using deep learning. Proceedings - IEEE Ultrasonics Symposium.