no code implementations • 17 Oct 2018 • Silas Nyboe Ørting, Jens Petersen, Laura H. Thomsen, Mathilde M. W. Wille, Marleen de Bruijne
We evaluate performance on 600 low-dose CT scans from the Danish Lung Cancer Screening Trial and find that learning from emphysema presence labels, which are much easier to obtain, gives equally good performance to learning from emphysema extent labels.
no code implementations • 19 Jun 2018 • Silas Nyboe Ørting, Jens Petersen, Veronika Cheplygina, Laura H. Thomsen, Mathilde M. W. Wille, Marleen de Bruijne
We evaluate the networks on 973 images, and show that the CNNs can learn disease relevant feature representations from derived similarity triplets.
no code implementations • 28 Oct 2016 • Francesco Ciompi, Kaman Chung, Sarah J. van Riel, Arnaud Arindra Adiyoso Setio, Paul K. Gerke, Colin Jacobs, Ernst Th. Scholten, Cornelia Schaefer-Prokop, Mathilde M. W. Wille, Alfonso Marchiano, Ugo Pastorino, Mathias Prokop, Bram van Ginneken
The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy.