Finally, Pre-trained ResNet Mixed Convolution was observed to be the best model in these experiments, achieving a macro F1-score of 0. 93 and a test accuracy of 96. 98\%, while at the same time being the model with the least computational cost.
It has been shown that the proposed framework can successfully reconstruct even for an acceleration factor of 20 for Cartesian (0. 968$\pm$0. 005) and 17 for radially (0. 962$\pm$0. 012) sampled data.
no code implementations • 25 Feb 2021 • Soumick Chatterjee, Alessandro Sciarra, Max Dünnwald, Raghava Vinaykanth Mushunuri, Ranadheer Podishetti, Rajatha Nagaraja Rao, Geetha Doddapaneni Gopinath, Steffen Oeltze-Jafra, Oliver Speck, Andreas Nürnberger
In this research, a deep learning based super-resolution technique is proposed and has been applied for DW-MRI.
Nonetheless during dynamic MRI, while achieving a high temporal resolution, the spatial resolution is compromised.
Traditional methods, such as prospective or retrospective motion correction, have been proposed to avoid or alleviate motion artefacts.
Segmentation of biomedical images can assist radiologists to make a better diagnosis and take decisions faster by helping in the detection of abnormalities, such as tumors.
2 code implementations • 18 Jun 2020 • Soumick Chatterjee, Kartik Prabhu, Mahantesh Pattadkal, Gerda Bortsova, Chompunuch Sarasaen, Florian Dubost, Hendrik Mattern, Marleen de Bruijne, Oliver Speck, Andreas Nürnberger
Blood vessels of the brain are providing the human brain with the required nutrients and oxygen.
The mean Micro-F1 score of the models for COVID-19 classifications ranges from 0. 66 to 0. 875, and is 0. 89 for the Ensemble of the network models.
1 code implementation • 17 Jan 2020 • A. Emre Kavur, N. Sinem Gezer, Mustafa Barış, Sinem Aslan, Pierre-Henri Conze, Vladimir Groza, Duc Duy Pham, Soumick Chatterjee, Philipp Ernst, Savaş Özkan, Bora Baydar, Dmitry Lachinov, Shuo Han, Josef Pauli, Fabian Isensee, Matthias Perkonigg, Rachana Sathish, Ronnie Rajan, Debdoot Sheet, Gurbandurdy Dovletov, Oliver Speck, Andreas Nürnberger, Klaus H. Maier-Hein, Gözde BOZDAĞI AKAR, Gözde Ünal, Oğuz Dicle, M. Alper Selver
The analysis shows that the performance of DL models for single modality (CT / MR) can show reliable volumetric analysis performance (DICE: 0. 98 $\pm$ 0. 00 / 0. 95 $\pm$ 0. 01) but the best MSSD performance remain limited (21. 89 $\pm$ 13. 94 / 20. 85 $\pm$ 10. 63 mm).
This deformation model was then applied to the high resolution images to obtain high resolution images of different breathing phases.
A multilevel random forest technique in a hierarchical way is proposed.