Generative Adversarial Networks For Data Scarcity Industrial Positron Images With Attention

1 INTRODUCTION . In recent years , with the advancement of science and technology , especially the rapid development of high-end manufacturing , in the field of industrial non-destructive testing , in many cases , it is necessary to perform defect detection without damaging or affecting the performance and internal structure of the device under test . Therefore , there is an increasing demand for corresponding detection devices . In complex industrial environments ( such as aviation , internal combustion engines , chemical engineering , etc . ) , it is of great research significance to detect faults and defects in closed chambers . In this paper , the use of positron annihilation gamma photon imaging positron emission imaging technology for industrial nondestructive testing is studied . The occurrence of positron annihilation is not affected by factors such as high temperature , high pressure , corrosion , etc. , so it can penetrate the dense metal material cavity , realize the undisturbed and non-destructive trace imaging of the detected object , and obtain the detected object after processing . Describe the image and perform a state analysis . Therefore , the quality of imaging technology directly affects the analysis of fault detection results . Positron Emission Tomography ( PET ) was first used in medical imaging . The principle is that when a radioactive positron nucleus decays , a proton in the nucleus is converted into a neutron , and a positron and a neutron are released . The positron will quickly combine with the electrons in the material in a very short time , causing a positron annihilation phenomenon , producing a pair of gamma photon pairs with opposite directions and energy of 511KeV . Photon pairs are collected , identified , processed , and finally reconstructed to obtain medical images . Commonly used PET reconstruction algorithms are analytic method ( K , 2000 ) and statistical method ( Shepp & Vardi , 2007 ) . The currently widely used algorithms are MLEM and OSEM . At present , PET technology has been widely used in the clinical diagnosis of human diseases . The advantages are quite obvious , the imaging quality is higher , and it shows great advantages in medical research . The principle of positron emission in industrial non-destructive fields is similar to medical imaging , but it has its own unique difficulties : the detection environment is more harsh , the sampling time is short , and at the same time , due to the phenomenon of scattering and attenuation of photons , industrial positron imaging is obtained . The image quality is even worse . Therefore , the reconstructed image needs to be further processed to obtain a higher quality image . In this paper , we propose adversarial networks of positron image memory module based on attention mechanism . Using medical images as basic data sets , introducing knowledge of migration learning , building memory module according to the contribution of detail features to images , a positron image generation network in the field of industrial non-destructive testing is obtained through joint training , thus achieving higher quality generation of industrial positron images . In summary , our main contributions in this paper are as follows : We are the first to advocate an idea of using Generative Adversarial Networks to enhance the detail of the positron image in the industrial non-destructive field , and realize the generation and processing of the scarce image data in the professional field . We use the medical CT image dataset as the basic training sample of the network framework , which is based on the idea of migration learning , and then extract the features of a small number of industrial non-destructively detected positron images , which can improve the details of the generated images , and make the network model have better applicability in the field of industrial non-destructive testing . We combine the attention-based mechanism in the professional domain image feature extraction . By constructing a memory module containing industrial positron image features , we can generate image generation in a specific domain , and finally conduct an industrial lossless positron image generation model . We train the whole network jointly , through the discriminant network of the antagonistic generation network , the front-end network was back-propagated , the input parameters were updated , and the model was optimized . Finally , the convergence was achieved and The Turing test was passed successfully . 2 RELATED WORK . Model of GAN : Since GAN ( Goodfellow et al. , 2014 ) was proposed in 2014 , it has become a research hotspot in the field of unsupervised learning , and related researches continue to increase . For the improvement of network structure , it mainly focuses on training stability and mode collapse . Radford et al.Radford et al . ( 2015 ) combine CNN with GAN to realize image generation , which makes GAN move from theory to practice , and establishes the framework and training mode of the whole GAN model . Arjovsky et al . M et al . ( 2017 ) propose Wasserstein GAN ( WGAN ) , which use Earth-Mover instead of JS divergence to measure the distance between the real sample and the generated sample distribution , and the problem of pattern collapse was well solved . Mirza proposed Conditional Generative Adversarial Nets ( CGAN ) in 2014M & S ( 2014 ) , which transformed unsupervised tasks into supervised tasks , thus improving the stability of training . Mao , X et al.X et al . ( 2016 ) propose Least Squares Generative Adversarial Networks ( LSGAN ) , which replace the loss function in the original network with the least squares loss function , alleviating the problem of training instability and insufficient diversity of generated images in GAN . Karras T et al.T et al . ( 2017 ) of NVIDIA team propose a progressive structure model to realize the transition from low-resolution to high-resolution image , and the generative model of high definition image can be trained smoothly . DeepMind team ( A et al. , 2018 ) introduce the idea of orthogonal regularization into GAN , which greatly improve the generation performance of GAN by truncating the input prior distribution Z in time , and contribute a lot of experience of parameter adjustment in the process of training mode . EBGAN ( J et al. , 2016 ) regards the discriminative network in GAN as an energy function and adds reconstruction error to it , which can solve the problem of mode collapse . Unrolled GAN ( L et al. , 2016 ) modifies the loss function of the generative network so that the generator can take into account the changes of discriminator after training K times and avoid the mode collapse caused by switching between different modes . DRAGAN ( N et al. , 2017 ) introduces the ” no regret algorithm ” in game theory , and construct gradient penalty scheme to avoid unwanted partial equilibrium . It can solve mode collapse problem and improve training efficiency . Domain Application : The greatest advantage of GAN is that it can generate real-like samples without any explicit modeling of data distribution in the whole generation process . Therefore , GAN has achieved good results in many fields such as image , text , voice and so on . Image generation ( P et al. , 2016 ) ( T et al. , 2017 ) ( Z et al. , 2017 ) , per resolution processing of image ( C et al. , 2017 ) , object detection ( Y et al. , 2018 ) ( J et al. , 2017 ) , object transfiguration ( S et al. , 2017 ) ( Wu H , 2017 ) , joint image generation ( Liu & Tuzel , 2016 ) , video generation ( Chen et al. , 2018 ) ( Tulyakov et al. , 2017 ) , text to image ( H et al. , 2017 ) ( A et al. , 2017 ) , text generation ( J et al. , 2018 ) , speech conversion ( C et al. , 2017 ) , domain adaptation ( J et al. , 2017 ) , and so on . These researches provide more possibilities for the practical application of GAN in the field . Medical Image Processing : Image generation in medical field can be divided into two categories : conditional and unconditional . ( M et al. , 2018 ) ( A & J , 2017 ) ( T et al. , 2017 ) uses DCGAN ( Radford et al. , 2015 ) to realize the batch generation of realistic medical images ( prostatic lesion plaques , lung cancer cakes , retina ) , and the resolution reaches 16 * 16,56 * 56 and 64 * 64 respectively , the results of which can pass the Turing test successfully . ( C et al. , 2018a ) uses DCGAN to learn higher resolution MR images distribution from a small number of samples , and compare them with the real images , so as to achieve the effect of ” false and true ” . ( C et al. , 2018b ) uses PGGAN to synthesize skin lesion images , and it successfully showed amazing and highly realistic synthetic images . Conditional medical image generation studies are as follows : ( D et al. , 2017 ) realizes CT image synthesis from MR images by means of 3D full convolution network . The loss function of the network is composed of reconstruction loss and gradient loss , and it is also trained by combining additional confrontation network to improve the reality of the synthesized images . ( P et al. , 2017 ) compress the images of vascular tree into a multivariate normal distribution by means of the antagonistic automatic encoder . The normal distribution is sampled to synthesize any high resolution vascular tree image , and then the end-to-end framework for high resolution retinal image synthesis is obtained by using image conversion model ( P et al. , 2016 ) . ( T et al. , 2017 ) proposes a two-stage GAN , the first one of which is to synthesize vessel tree images from noise training , and the second network uses image translation framework to generate real , high-resolution pairs of groundtruth vessel segmentation and the corresponding eye fundus image . ( W et al. , 2018 ) uses CGAN as the framework to synthesize 200 * 200 PET images by using CT image and binary label graph . The researchs contribution lies in the construction of a two-channel generation network to achieve a more realistic global output and it called multi-channel GAN . ( F & D , 2018 ) sets up a multi-stage generator to obtain speckle images , low-resolution images , and high-resolution images in turn by intra-vascular ultrasound simulation of tissue maps according to different generating networks . ( A & G , 2017 ) conducts joint learning by adding a specific task network to CGAN , and then obtain a network model that retain specific task characteristics . ( M et al. , 2018 ) uses WGAN as the network framework , and use noise and attribute vectors as inputs to generating high-resolution 3D images . In addition , in the aspects of image segmentation , reconstruction , detection , denoising , recognition , classification , etc , and the use of GAN model has also achieved some research results , which provides feasibility for the application of GAN in professional and specific field . Traditional positron image processing in the field of industrial nondestructive processing focuses on the improvement of image reconstruction algorithm , but due to the inherent characteristics of small sampling points and short time , the quality of the obtained image is poor , and the stability of the image affected by the field is poor . Therefore , in this paper , we innovatively introduce the knowledge of neural network for positron image processing . And through the experimental verification , a high quality image is obtained .

Summary: This work proposes a method to transfer information from PET imaging data from the medical domain, where data is highly available, to the industrial one, where data is scarce, in the context of non-destructive material quality evaluation. The basic idea seems interesting, but unfortunately in the present form he paper is very difficult to appreciate, as it lacks of important details concerning methodology, experimental results, and comparison with respect to the state-of-the-art. Moreover, the manuscript still appears in a draft form. Sentences are often broken, the text presents many typos and grammar mistakes, and the citations are not understandable.