  Automated detection and segmentation of pulmonary nodules on lung computed
tomography (CT) scans can facilitate early lung cancer diagnosis. Existing
supervised approaches for automated nodule segmentation on CT scans require
voxel-based annotations for training, which are labor- and time-consuming to
obtain. In this work, we propose a weakly-supervised method that generates
accurate voxel-level nodule segmentation trained with image-level labels only.
By adapting a convolutional neural network (CNN) trained for image
classification, our proposed method learns discriminative regions from the
activation maps of convolution units at different scales, and identifies the
true nodule location with a novel candidate-screening framework. Experimental
results on the public LIDC-IDRI dataset demonstrate that, our weakly-supervised
nodule segmentation framework achieves competitive performance compared to a
fully-supervised CNN-based segmentation method.
