  The insufficiency of annotated medical imaging scans for cancer makes it
challenging to train and validate data-hungry deep learning models in precision
oncology. We propose a new richer generative adversarial network for free-form
3D tumor/lesion synthesis in computed tomography (CT) images. The network is
composed of a new richer convolutional feature enhanced dilated-gated generator
(RicherDG) and a hybrid loss function. The RicherDG has dilated-gated
convolution layers to enable tumor-painting and to enlarge perceptive fields;
and it has a novel richer convolutional feature association branch to recover
multi-scale convolutional features especially from uncertain boundaries between
tumor and surrounding healthy tissues. The hybrid loss function, which consists
of a diverse range of losses, is designed to aggregate complementary
information to improve optimization.
  We perform a comprehensive evaluation of the synthesis results on a wide
range of public CT image datasets covering the liver, kidney tumors, and lung
nodules. The qualitative and quantitative evaluations and ablation study
demonstrated improved synthesizing results over advanced tumor synthesis
methods.
