Lung Nodule Detection

Lung cancer is the deadliest cancer worldwide. It has been shown that early detection using computer tomography (CT) scans can reduce deaths caused by this disease. We present a general framework for the detection of lung cancer in chest CT images. Our method consists of a nodule detector trained on the LIDC-IDRI dataset.
Our candidate extraction approach is effective to produce precise candidates with a recall of 99.6%. In addition, false positive reduction stage manages to successfully classify candidates and increases precision by a factor of 7.000.
Figure 1. Qualitative results of high scored nodule detections.

Publications

Automated detection of lung nodules with three-dimensional convolutional neural networks

G. Pérez and P. Arbeláez

13th International Conference on Medical Information Processing and Analysis (SIPAIM), 2017

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Citation

@article{PerezSPIE2017,
author = "{G. Perez and P. Arbelaez}",
title = "{Automated detection of lung nodules with three-dimensional convolutional neural networks}",
journal = "{Proc.SPIE}",
volume = {10572},
year = {2017},
}
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