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   Articles with "adversarial learning" as a keyword



A medical image classification method based on self-regularized adversarial learning.

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recommendations! Published in 2024 at "Medical physics"

DOI: 10.1002/mp.17320

Abstract: BACKGROUND Deep learning (DL) techniques have been extensively applied in medical image classification. The unique characteristics of medical imaging data present challenges, including small labeled datasets, severely imbalanced class distribution, and significant variations in imaging… read more here.

Keywords: classification; adversarial learning; image; medical image ... See more keywords

Histogram matching‐enhanced adversarial learning for unsupervised domain adaptation in medical image segmentation

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recommendations! Published in 2025 at "Medical Physics"

DOI: 10.1002/mp.17757

Abstract: Unsupervised domain adaptation (UDA) seeks to mitigate the performance degradation of deep neural networks when applied to new, unlabeled domains by leveraging knowledge from source domains. In medical image segmentation, prevailing UDA techniques often utilize… read more here.

Keywords: adversarial learning; adaptation; unsupervised domain; segmentation ... See more keywords

Dynamically occluded samples via adversarial learning for person re-identification in sensor networks

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recommendations! Published in 2021 at "Ad Hoc Networks"

DOI: 10.1016/j.adhoc.2020.102316

Abstract: Abstract In this paper, we propose a novel data augmentation method to dynamically learn occluded samples via adversarial learning for person re-identification (re-ID) in sensor networks. Specifically, we design two CNN models to learn original-image… read more here.

Keywords: image features; occluded samples; samples via; image ... See more keywords
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Segmentation and quantification of infarction without contrast agents via spatiotemporal generative adversarial learning

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recommendations! Published in 2020 at "Medical image analysis"

DOI: 10.1016/j.media.2019.101568

Abstract: Accurate and simultaneous segmentation and full quantification (all indices are required in a clinical assessment) of the myocardial infarction (MI) area are crucial for early diagnosis and surgical planning. Current clinical methods remain subject to… read more here.

Keywords: contrast; segmentation; infarction; segmentation quantification ... See more keywords
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Domain adaptation based on domain-invariant and class-distinguishable feature learning using multiple adversarial networks

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recommendations! Published in 2020 at "Neurocomputing"

DOI: 10.1016/j.neucom.2020.06.044

Abstract: Abstract Adversarial networks have been used to learn transferable representations in many domain adaptation methods. However, there is no theoretical guarantee that two distributions are identical, even if the discriminator is fully confused. Therefore, a… read more here.

Keywords: adversarial networks; class; domain adversarial; central samples ... See more keywords
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Adversarial-learning-based image-to-image transformation: A survey

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recommendations! Published in 2020 at "Neurocomputing"

DOI: 10.1016/j.neucom.2020.06.067

Abstract: Abstract Recently, the generative adversarial network (GAN) has attracted wide attention for various computer vision tasks. GAN provides a novel concept for image-to-image transformation by means of adversarial learning. In recent years, numerous adversarial-learning-based methods… read more here.

Keywords: image; image transformation; image image; learning based ... See more keywords

Improving microvascular brain analysis with adversarial learning for OCT–TPM vascular domain translation

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recommendations! Published in 2025 at "Scientific Reports"

DOI: 10.1038/s41598-025-07410-x

Abstract: Modeling microscopic cerebrovascular networks is essential for understanding cerebral blood flow and oxygen transport. High-resolution imaging modalities, such as Optical Coherence Tomography (OCT) and Two-Photon Microscopy (TPM), are widely used to capture microvascular structure and… read more here.

Keywords: adversarial learning; image; analysis; tpm vascular ... See more keywords

Polarimetric image recovery method with domain-adversarial learning for underwater imaging

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recommendations! Published in 2025 at "Scientific Reports"

DOI: 10.1038/s41598-025-86529-3

Abstract: Underwater imaging is significant but the images are always subject to degradation, which varies in different underwater environments. Factors such as light scattering, absorption, and environmental noise can affect the quality of underwater images, leading… read more here.

Keywords: adversarial learning; domain adversarial; underwater imaging; image ... See more keywords

Regularized adversarial learning for normalization of multi-batch untargeted metabolomics data

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recommendations! Published in 2023 at "Bioinformatics"

DOI: 10.1093/bioinformatics/btad096

Abstract: Abstract Motivation Untargeted metabolomics by mass spectrometry is the method of choice for unbiased analysis of molecules in complex samples of biological, clinical or environmental relevance. The exceptional versatility and sensitivity of modern high-resolution instruments… read more here.

Keywords: untargeted metabolomics; multi batch; regularized adversarial; adversarial learning ... See more keywords

Robust quantum control in games: An adversarial learning approach

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recommendations! Published in 2020 at "Physical Review A"

DOI: 10.1103/physreva.101.052317

Abstract: High-precision operation of quantum computing systems must be robust to uncertainties and noises in the quantum hardware. In this paper, we show that through a game played between the uncertainties (or noises) and the controls,… read more here.

Keywords: adversarial learning; robust quantum; quantum control; approach ... See more keywords

Adversarial Learning for Invertible Steganography

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recommendations! Published in 2020 at "IEEE Access"

DOI: 10.1109/access.2020.3034936

Abstract: Deep neural networks have revolutionised the research landscape of steganography. However, their potential has not been explored in invertible steganography, a special class of methods that permits the recovery of distorted objects due to steganographic… read more here.

Keywords: invertible steganography; method; steganography; learning invertible ... See more keywords