An Enhanced Priori Knowledge GAN for CT Images Generation of Early Lung Nodules with Small-Size Labelled Samples

Abstract

The small size of labelled samples is one of the challenging problems in identifying early lung nodules from CT images using deep learning methods. Recent literature on the topic shows that deep convolutional generative adversarial network (DCGAN) has been used in medical data synthesis and gained some success, but does not demonstrate satisfactory results in synthesizing CT images. It primarily suffers from the problem of model convergence and is prone to mode collapse. In this paper, we propose a generative adversarial network (GAN) model with prior knowledge to generate CT images of early lung nodules from a small-size of labelled samples, i.e., SLS-PriGAN. Particularly, a knowledge acquisition network and a sharpening network are designed for priori knowledge learning and acquisition, and then, a GAN model is developed to produce CT images of early lung nodules. To validate our method, a general fast R-CNN network is trained using the CT images generated by SLS-PriGAN. The experiment result shows that it achieved a recognizing accuracy of 91%, a recall rate of 81%, and F 1 score of 0.85 in identifying clinic CT images of early lung nodules. This provides a promising way of identifying early lung nodules from CT images using deep learning with small-size labelled samples.

Automatic Detection and Segmentation of Ovarian Cancer Using a Multitask Model in Pelvic CT Images

“…IDTechEx, a wellknown British research company, predicts that the market for image-based artificial intelligence medical diagnosis will grow by nearly 10000% by 2040. So far, deep learning has been widely used in the diagnosis of many diseases, such as breast cancer screening, benign and malignant thyroid nodules, and lung cancer detection [11][12][13][14][15].…”

Retracted: An Enhanced Priori Knowledge GAN for CT Images Generation of Early Lung Nodules with Small-Size Labelled Samples

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”