no code implementations • 20 Aug 2020 • Zachary M. C. Baum, Ester Bonmati, Lorenzo Cristoni, Andrew Walden, Ferran Prados, Baris Kanber, Dean C. Barratt, David J. Hawkes, Geoffrey J M Parker, Claudia A M Gandini Wheeler-Kingshott, Yipeng Hu
The diagnosis assistance module can then be trained with data that are deemed of sufficient quality, guaranteed by the closed-loop feedback mechanism from the quality assessment module.
The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2, emerged into a world being rapidly transformed by artificial intelligence (AI) based on big data, computational power and neural networks.
Purpose: This paper proposes a pipeline to acquire a scalar tapering measurement from the carina to the most distal point of an individual airway visible on CT. We show the applicability of using tapering measurements on clinically acquired data by quantifying the reproducibility of the tapering measure.
We propose a simple measurement of tapering along the airways to diagnose and monitor bronchiectasis.
Multi-task neural network architectures provide a mechanism that jointly integrates information from distinct sources.
no code implementations • 9 Feb 2018 • Timur Kuzhagaliyev, Neil T. Clancy, Mirek Janatka, Kevin Tchaka, Francisco Vasconcelos, Matthew J. Clarkson, Kurinchi Gurusamy, David J. Hawkes, Brian Davidson, Danail Stoyanov
Irreversible electroporation (IRE) is a soft tissue ablation technique suitable for treatment of inoperable tumours in the pancreas.
Furthermore, we evaluate the matching of the reconstructed polyp from OC with other colonic endoluminal surface structures such as haustral folds and show that there is a minimum at the correct polyp from CTC.
We evaluate our methods using various computational digital phantoms, uncompressed breast MR images, and in-vivo DBT simulations.