3 papers
eess.IV2019
Quantitative Impact of Label Noise on the Quality of Segmentation of Brain Tumors on MRI scans
Michał Marcinkiewicz, Grzegorz Mrukwa
Over the last few years, deep learning has proven to be a great solution to many problems, such as image or text classification. Recently, deep learning-based solutions have outper…
eess.IV2019
Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors
Jakub Nalepa, Pablo Ribalta Lorenzo, Michal Marcinkiewicz +8
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in diagnosis and grading of brain tumor. Although manual DCE biomarker extraction algorithms…
cs.CV2018
Band Selection from Hyperspectral Images Using Attention-based Convolutional Neural Networks
Pablo Ribalta Lorenzo, Lukasz Tulczyjew, Michal Marcinkiewicz +1
This paper introduces new attention-based convolutional neural networks for selecting bands from hyperspectral images. The proposed approach re-uses convolutional activations at di…