Hierarchical Mixtures of Generators for Adversarial Learning

Ahmetoğlu, Alper; Alpaydın, Ethem

doi:10.1109/icpr48806.2021.9413249

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Here, we use adversarial learning to enable z c to include common features of EEG signals, but not class-specific features. Unlike other studies [54], [55], no generator is required because input data distributions are not utilized in this study. The training objective is to train f c for extraction of common features.…”

Section: B Adversarial Learningmentioning

confidence: 99%

Factorization Approach for Sparse Spatio-Temporal Brain-Computer Interface

Lee¹,

Cho²,

Kwon³

et al. 2022

Preprint

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Recently, advanced technologies have unlimited potential in solving various problems with a large amount of data. However, these technologies have yet to show competitive performance in brain-computer interfaces (BCIs) which deal with brain signals. Basically, brain signals are difficult to collect in large quantities, in particular, the amount of information would be sparse in spontaneous BCIs. In addition, we conjecture that high spatial and temporal similarities between tasks increase the prediction difficulty. We define this problem as sparse condition.To solve this, a factorization approach is introduced to allow the model to obtain distinct representations from latent space. To this end, we propose two feature extractors: A class-common module is trained through adversarial learning acting as a generator; Class-specific module utilizes loss function generated from classification so that features are extracted with traditional methods. To minimize the latent space shared by the classcommon and class-specific features, the model is trained under orthogonal constraint. As a result, EEG signals are factorized into two separate latent spaces. Evaluations were conducted on a single-arm motor imagery dataset. From the results, we demonstrated that factorizing the EEG signal allows the model to extract rich and decisive features under sparse condition.

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Section: B Adversarial Learningmentioning

confidence: 99%

Factorization Approach for Sparse Spatio-Temporal Brain-Computer Interface

Lee¹,

Cho²,

Kwon³

et al. 2022

Preprint

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“…When working with high-dimensional data, including images and text, this technique becomes more difficult. In order to achieve this, we employ generative adversarial networks [ 5 ], which offer a mapping from the latent space to the high-dimensional data. GAN’s capacity to extract information from all types of image data has led to more promising findings in the segmentation of MRI data, such as when using MRI data to segment CT images [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Generative Adversarial Network-Based Approach for Automated Brain Tumour Segmentation

et al. 2023

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Background: Medical image segmentation is more complicated and demanding than ordinary image segmentation due to the density of medical pictures. A brain tumour is the most common cause of high mortality. Objectives: Extraction of tumorous cells is particularly difficult due to the differences between tumorous and non-tumorous cells. In ordinary convolutional neural networks, local background information is restricted. As a result, previous deep learning algorithms in medical imaging have struggled to detect anomalies in diverse cells. Methods: As a solution to this challenge, a deep convolutional generative adversarial network for tumour segmentation from brain Magnetic resonance Imaging (MRI) images is proposed. A generator and a discriminator are the two networks that make up the proposed model. This network focuses on tumour localisation, noise-related issues, and social class disparities. Results: Dice Score Coefficient (DSC), Peak Signal to Noise Ratio(PSNR), and Structural Index Similarity (SSIM) are all generally 0.894, 62.084 dB, and 0.88912, respectively. The model’s accuracy has improved to 97 percent, and its loss has reduced to 0.012. Conclusions: Experiments reveal that the proposed approach may successfully segment tumorous and benign tissues. As a result, a novel brain tumour segmentation approach has been created.

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