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Titel:

Lung nodule detection in chest X-rays using synthetic ground-truth data comparing CNN-based diagnosis to human performance.

Dokumenttyp:
Journal Article
Autor(en):
Schultheiss, Manuel; Schmette, Philipp; Bodden, Jannis; Aichele, Juliane; Müller-Leisse, Christina; Gassert, Felix G; Gassert, Florian T; Gawlitza, Joshua F; Hofmann, Felix C; Sasse, Daniel; von Schacky, Claudio E; Ziegelmayer, Sebastian; De Marco, Fabio; Renger, Bernhard; Makowski, Marcus R; Pfeiffer, Franz; Pfeiffer, Daniela
Abstract:
We present a method to generate synthetic thorax radiographs with realistic nodules from CT scans, and a perfect ground truth knowledge. We evaluated the detection performance of nine radiologists and two convolutional neural networks in a reader study. Nodules were artificially inserted into the lung of a CT volume and synthetic radiographs were obtained by forward-projecting the volume. Hence, our framework allowed for a detailed evaluation of CAD systems' and radiologists' performance due to...     »
Zeitschriftentitel:
Sci Rep
Jahr:
2021
Band / Volume:
11
Heft / Issue:
1
Volltext / DOI:
doi:10.1038/s41598-021-94750-z
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/34349135
Print-ISSN:
2045-2322
TUM Einrichtung:
Institut für Diagnostische und Interventionelle Radiologie
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