scholarly journals Generative Adversarial Network (GANs) based training set enhancement for Stomach Adenocarcinoma Computed Tomography (CT) scan

2019 ◽  
Vol 160 ◽  
pp. 377-384
Author(s):  
Paawan Sharma ◽  
Kartik Patel ◽  
Sourabh Kuvera ◽  
Fenil Dankhara
Keyword(s):  
Computed Tomography ◽  
Ct Scan ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Stomach Adenocarcinoma

scholarly journals Free-form tumor synthesis in computed tomography images via richer generative adversarial network

2021 ◽  
Vol 218 ◽  
pp. 106753
Author(s):  
Qiangguo Jin ◽  
Hui Cui ◽  
Changming Sun ◽  
Zhaopeng Meng ◽  
Ran Su
Keyword(s):  
Computed Tomography ◽  
Free Form ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Computed Tomography Images

scholarly journals 1D conditional generative adversarial network for spectrum-to-spectrum translation of simulated chemical reflectance signatures

2021 ◽  
Author(s):  
Cara Murphy ◽  
John Kerekes
Keyword(s):  
Classification Accuracy ◽  
Domain Adaptation ◽  
Real Data ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Average Classification Accuracy ◽  
Adversarial Network ◽  
Chemical Residues ◽  
Reflectance Data

The classification of trace chemical residues through active spectroscopic sensing is challenging due to the lack of physics-based models that can accurately predict spectra. To overcome this challenge, we leveraged the field of domain adaptation to translate data from the simulated to the measured domain for training a classifier. We developed the first 1D conditional generative adversarial network (GAN) to perform spectrum-to-spectrum translation of reflectance signatures. We applied the 1D conditional GAN to a library of simulated spectra and quantified the improvement in classification accuracy on real data using the translated spectra for training the classifier. Using the GAN-translated library, the average classification accuracy increased from 0.622 to 0.723 on real chemical reflectance data, including data from chemicals not included in the GAN training set.

scholarly journals Promising Generative Adversarial Network Based Sinogram Inpainting Method for Ultra-Limited-Angle Computed Tomography Imaging

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3941 ◽  
Author(s):  
Li ◽  
Cai ◽  
Wang ◽  
Zhang ◽  
Tang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Imaging System ◽  
Reconstruction Method ◽  
Ct Reconstruction ◽  
Reconstruction Algorithms ◽  
Generative Adversarial Network ◽  
Image Domain ◽  
Adversarial Network ◽  
Domain Loss ◽  
Limited Angle

Limited-angle computed tomography (CT) image reconstruction is a challenging problem in the field of CT imaging. In some special applications, limited by the geometric space and mechanical structure of the imaging system, projections can only be collected with a scanning range of less than 90°. We call this kind of serious limited-angle problem the ultra-limited-angle problem, which is difficult to effectively alleviate by traditional iterative reconstruction algorithms. With the development of deep learning, the generative adversarial network (GAN) performs well in image inpainting tasks and can add effective image information to restore missing parts of an image. In this study, given the characteristic of GAN to generate missing information, the sinogram-inpainting-GAN (SI-GAN) is proposed to restore missing sinogram data to suppress the singularity of the truncated sinogram for ultra-limited-angle reconstruction. We propose the U-Net generator and patch-design discriminator in SI-GAN to make the network suitable for standard medical CT images. Furthermore, we propose a joint projection domain and image domain loss function, in which the weighted image domain loss can be added by the back-projection operation. Then, by inputting a paired limited-angle/180° sinogram into the network for training, we can obtain the trained model, which has extracted the continuity feature of sinogram data. Finally, the classic CT reconstruction method is used to reconstruct the images after obtaining the estimated sinograms. The simulation studies and actual data experiments indicate that the proposed method performed well to reduce the serious artifacts caused by ultra-limited-angle scanning.

scholarly journals A De Novo Molecular Generation Method Using Latent Vector Based Generative Adversarial Network

2019 ◽  
Author(s):  
Oleksii Prykhodko ◽  
Simon Viet Johansson ◽  
Panagiotis-Christos Kotsias ◽  
Esben Jannik Bjerrum ◽  
Ola Engkvist ◽  
...  
Keyword(s):  
Deep Learning ◽  
De Novo ◽  
Chemical Space ◽  
Learning Method ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Structure Generation ◽  
Adversarial Network ◽  
Molecule Design ◽  
Novel Structures

<p>Recently deep learning method has been used for generating novel structures. In the current study, we proposed a new deep learning method, LatentGAN, which combine an autoencoder and a generative adversarial neural network for doing de novo molecule design. We applied the method for structure generation in two scenarios, one is to generate random drug-like compounds and the other is to generate target biased compounds. Our results show that the method works well in both cases, in which sampled compounds from the trained model can largely occupy the same chemical space of the training set and still a substantial fraction of the generated compound are novel. The distribution of drug-likeness score for compounds sampled from LatentGAN is also similar to that of the training set.</p>

scholarly journals A De Novo Molecular Generation Method Using Latent Vector Based Generative Adversarial Network

2019 ◽  
Author(s):  
Oleksii Prykhodko ◽  
Simon Viet Johansson ◽  
Panagiotis-Christos Kotsias ◽  
Josep Arús-Pous ◽  
Esben Jannik Bjerrum ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
De Novo Molecular Design ◽  
Novel Structures

<p> </p><p>Deep learning methods applied to drug discovery have been used to generate novel structures. In this study, we propose a new deep learning architecture, LatentGAN, which combines an autoencoder and a generative adversarial neural network for de novo molecular design. We applied the method in two scenarios: one to generate random drug-like compounds and another to generate target-biased compounds. Our results show that the method works well in both cases: sampled compounds from the trained model can largely occupy the same chemical space as the training set and also generate a substantial fraction of novel compounds. Moreover, the drug-likeness score of compounds sampled from LatentGAN is also similar to that of the training set. Lastly, generated compounds differ from those obtained with a Recurrent Neural Network-based generative model approach, indicating that both methods can be used complementarily.</p><p> </p>

Data Augmentation based on Sequence Generative Adversarial Network for Chinese Clinical Named Entity Recognition (Preprint)

2020 ◽  
Author(s):  
蓬辉 王
Keyword(s):  
Data Augmentation ◽  
Named Entity Recognition ◽  
Basic Element ◽  
Entity Recognition ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Named Entity ◽  
Adversarial Network ◽  
External Resource ◽  
Medical Domain

BACKGROUND Chinese clinical named entity recognition, as a fundamental task of Chinese medical information extraction, plays an important role in recognizing medical entities contained in Chinese electronic medical records. Limited to lack of large annotated data, existing methods concentrate on employing external resources to improve the performance of clinical named entity recognition, which require lots of time and efficient rules to add external resources. OBJECTIVE To solve the problem of lack of large annotated data, we employ data augmentation without external resource to automatically generate more medical data depending on entities and non-entities in the training set, and enlarge training dataset to improve the performance of named entity recognition. METHODS In this paper, we propose a method of data augmentation, based on sequence generative adversarial network, to enlarge the training set. Different from other sequence generative adversarial networks, where the basic element is character or word, the basic element of our generated sequence is entity or non-entity. In our model, the generator can generate new sentences composed of entities and non-entities based on the learned hidden relationship between the entities and non-entities in the training set and the discriminator can judge if the generated sentences are positive and give rewards to help train the generator. The generated data from sequence adversarial network is used to enlarge the training set and improve the performance of named entity recognition in medical records. RESULTS Without external resource, we employ our data augmentation method in three datasets, both in general domains and medical domain. Experiments show that when we use generated data from data augmentation to expand training set, named entity recognition system has achieved competitive performance compared with existing methods, which shows the effectiveness of our data augmentation method. In general domains, our method achieves an overall F1-score of 59.42% in Weibo NER dataset and a F1-score of 95.28% in Resume. In medical domain, our method achieves 83.40%. CONCLUSIONS Our data augmentation method can expand training set based on the hidden relationship between entities and non-entities in the dataset, which can alleviate the problem of lack of labeled data while avoid using external resource. At the same time, our method can improve the performance of named entity recognition not only in general domains but also medical domain.

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