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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Though large-scale datasets are essential for training deep learning systems,it is expensive to scale up the collection of medical imaging datasets.Synthesizing the objects of interests, such as lung nodules, in medical imagesbased on the distribution of annotated datasets can be helpful for improvingthe supervised learning tasks, especially when the datasets are limited by sizeand class balance. In this paper, we propose the class-aware adversarialsynthesis framework to synthesize lung nodules in CT images. The framework isbuilt with a coarse-to-fine patch in-painter (generator) and two class-awarediscriminators. By conditioning on the random latent variables and the targetnodule labels, the trained networks are able to generate diverse nodules giventhe same context. By evaluating on the public LIDC-IDRI dataset, we demonstratean example application of the proposed framework for improving the accuracy ofthe lung nodule malignancy estimation as a binary classification problem, whichis important in the lung screening scenario. We show that combining the realimage patches and the synthetic lung nodules in the training set can improvethe mean AUC classification score across different network architectures by 2%.

Class-Aware Adversarial Lung Nodule Synthesis in CT Images