Kostopoulos Evangelos (Phd Candidate)
- Thesis title: Neural network pruning methods and their application on medical imaging
- Supervisor: Diamantaras Konstantinos
- Advisory Committee Members:
- Ioanna Chouvarda, Professor AUTH
Konstantinos Goulianas, Associate Professor IHU
For the past few years there have been several attempts to use convolutional neural networks so as to analyze and process medical images (CT, MRI, X-RAY) with very promising results. However, due to missing or incorrect information, improving and restoring images using filtering and finding morphological patterns can be very hard. This PhD will research the application of pruning methods in terms of medical images’ analysis and focus on the design of a new pruning technique. This technique will simulate the human optic nerve, which isolates and recognizes objects using pruning and repeating methods during the entire recognition process. The goal is to improve the performance and reliability of this predictive process, as well as reduce the margin of error in terms of the prediction process that relates to medical image analysis.