21 citations · 30 across the 4 of their papers we have counts for
4 papers
Optimized U-Net for Brain Tumor Segmentation
Michał Futrega, Alexandre Milesi, Michal Marcinkiewicz +1
We propose an optimized U-Net architecture for a brain tumor segmentation task in the BraTS21 challenge. To find the optimal model architecture and the learning schedule, we have r…
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…
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…
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…