Synthetic Lung Nodule 3D Image Generation Using Autoencoders
This work addresses data scarcity in automated lung cancer diagnosis, though it is incremental as it builds on existing autoencoder techniques.
The authors tackled the problem of limited medical image data for training machine learning models by developing an automatic synthetic lung nodule 3D image generator using autoencoders, which successfully produced quality synthetic images as demonstrated in experiments.
One of the challenges of using machine learning techniques with medical data is the frequent dearth of source image data on which to train. A representative example is automated lung cancer diagnosis, where nodule images need to be classified as suspicious or benign. In this work we propose an automatic synthetic lung nodule image generator. Our 3D shape generator is designed to augment the variety of 3D images. Our proposed system takes root in autoencoder techniques, and we provide extensive experimental characterization that demonstrates its ability to produce quality synthetic images.