  Computer-aided diagnosis system for diffuse lung diseases (DLDs) is necessary
for the objective assessment of the lung diseases. In this paper, we develop
semantic segmentation model for 5 kinds of DLDs. DLDs considered in this work
are consolidation, ground glass opacity, honeycombing, emphysema, and normal.
Convolutional neural network (CNN) is one of the most promising technique for
semantic segmentation among machine learning algorithms. While creating
annotated dataset for semantic segmentation is laborious and time consuming,
creating partially annotated dataset, in which only one chosen class is
annotated for each image, is easier since annotators only need to focus on one
class at a time during the annotation task. In this paper, we propose a new
weak supervision technique that effectively utilizes partially annotated
dataset. The experiments using partially annotated dataset composed 372 CT
images demonstrated that our proposed technique significantly improved
segmentation accuracy.
