Nodule detection and generation on chest X-rays: NODE21 Challenge

no code implementations • 4 Jan 2024 • Ecem Sogancioglu, Bram van Ginneken, Finn Behrendt, Marcel Bengs, Alexander Schlaefer, Miron Radu, Di Xu, Ke Sheng, Fabien Scalzo, Eric Marcus, Samuele Papa, Jonas Teuwen, Ernst Th. Scholten, Steven Schalekamp, Nils Hendrix, Colin Jacobs, Ward Hendrix, Clara I Sánchez, Keelin Murphy

To address this, we organized a public research challenge, NODE21, aimed at the detection and generation of lung nodules in chest X-rays.

Benchmarking

Kidney abnormality segmentation in thorax-abdomen CT scans

no code implementations • 6 Sep 2023 • Gabriel Efrain Humpire Mamani, Nikolas Lessmann, Ernst Th. Scholten, Mathias Prokop, Colin Jacobs, Bram van Ginneken

Our end-to-end segmentation method was trained on 215 contrast-enhanced thoracic-abdominal CT scans, with half of these scans containing one or more abnormalities.

Segmentation

Automated Estimation of Total Lung Volume using Chest Radiographs and Deep Learning

no code implementations • 3 May 2021 • Ecem Sogancioglu, Keelin Murphy, Ernst Th. Scholten, Luuk H. Boulogne, Mathias Prokop, Bram van Ginneken

The optimal models were tested on 291 CXR studies with reference lung volume obtained from PFT.

Towards automatic pulmonary nodule management in lung cancer screening with deep learning

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.

Lung Nodule Classification Lung Nodule Detection +2