English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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

Weak Supervision in Convolutional Neural Network for Semantic Segmentation of Diffuse Lung Diseases Using Partially Annotated Dataset