Franciszek Binczyk

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We propose a new method that employs transfer learning techniques to effectively correct sampling selection errors introduced by sparse annotations during supervised learning for automated tumor segmentation. The practicality of current learning-based automated tissue classification approaches is severely impeded by their dependency on manually segmented(More)
UNLABELLED Nuclear Magnetic Resonance (NMR) spectroscopy is a popular medical diagnostic technique. NMR is also the favourite tool of chemists/biochemists to elucidate the molecular structure of small or big molecules; it is also a widely used tool in material science, in food science etc. In the case of medical diagnosis it allows for determining a(More)
The aim of the paper was to develop the techniques for automatic tuning of the most popular phase correction algorithms used in NMR spectrum analysis. They were subjected to the parameter optimization and a set of the efficient automatic phase correction algorithms was constructed, resulting in significant increase of the phase error correction accuracy.(More)