Medical Image Compression Based on Variational Autoencoder

Liu, Xuan; Zhang, Lu; Guo, Zihao; Han, Tong; Ju, Mingchi; Xu, Bo; Liu, Hong

doi:10.1155/2022/7088137

Cited by 12 publications

(3 citation statements)

References 36 publications

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“…Liu et al [11] devised a medical image compression technique that effectively balances compression and reconstruction. By leveraging a residual network module and a variational autoencoder, they concurrently optimize the rate and distortion.…”

Section: Related Workmentioning

confidence: 99%

Efficient MRI Image Real-Time Processing Using FPGA-Based IIR Filters

Hamad,

Ismail,

Raed

2024

JESA

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Section: Related Workmentioning

confidence: 99%

Efficient MRI Image Real-Time Processing Using FPGA-Based IIR Filters

Hamad,

Ismail,

Raed

2024

JESA

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“…We also include the latent representation from a β-Variational Autoencoder (β-VAE) [15] as a more sophisticated baseline. Deep autoencoder models have been explored as tools to learn better image and video compression algorithms for technological applications [16,17], as well as to model human visual memory [18][19][20]. In addition to baseline models, we consider networks trained on the ILSVRC ImageNet classification challenge (both the 1,000-way and 22,000-way versions) and networks trained on the Contrastive Language-Image Pre-training (CLIP) objective [21].…”

Section: Continuous Report With Natural Imagesmentioning

confidence: 99%

Scaling models of visual working memory to natural images

Bates

Alvarez

Gershman

2023

Preprint

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Over the last few decades, psychologists have developed precise quantitative models of human recall performance in visual working memory (VWM) tasks. However, these models are tailored to a particular class of artificial stimulus displays and simple feature reports from participants (e.g., the color or orientation of a simple object). Our work has two aims. The first is to build models that explain people's memory errors in continuous report tasks with natural images. Here, we use image generation algorithms to generate continuously varying response alternatives that differ from the stimulus image in natural and complex ways, in order to capture the richness of people's stored representations. The second aim is to determine whether models that do a good job of explaining memory errors with natural images also explain errors in the more heavily studied domain of artificial displays with simple items. We find that: (i) features taken from state-of-the-art deep encoders explain coarse-grained and some fine-grained aspects of trial-by-trial difficulty in natural images, while several reasonable base- lines do not; and (ii) deep visual encoders could reproduce set-size effects but overall offered a poorer explanation of human data in the artificial domain. Together, our results suggest that people may rely on distinct cognitive systems or brain areas in artificial versus natural task domains. Moving forward, our approach offers a scalable way to build a more generalized understanding of VWM representations by combining recent advances in both AI and cognitive modeling.

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“…With the image dimension compression or reduction process, the resulting DNA sequence will have a much smaller number compared to images that are not compressed. Image compression using a generative model has been carried out by Liu et al [55] with predetermined input images.…”

Section: Rq5 How Can Generative Models Be Implemented In Compression ...mentioning

confidence: 99%

Analysis and review of the possibility of using the generative model as a compression technique in DNA data storage: review and future research agenda

Muttaqin,

Herdiyeni,

Buono

et al. 2023

Int. J. Adv. Intell. Informatics

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The amount of data in this world is getting higher, and overwriting technology also has severe challenges. Data growth is expected to grow to 175 ZB by 2025. Data storage technology in DNA is an alternative technology with potential in information storage, mainly digital data. One of the stages of storing information on DNA is synthesis. This synthesis process costs very high, so it is necessary to integrate compression techniques for digital data to minimize the costs incurred. One of the models used in compression techniques is the generative model. This paper aims to see if compression using this generative model allows it to be integrated into data storage methods on DNA. To this end, we have conducted a Systematic Literature Review using the PRISMA method in selecting papers. We took the source of the papers from four leading databases and other additional databases. Out of 2440 papers, we finally decided on 34 primary papers for detailed analysis. This systematic literature review (SLR) presents and categorizes based on research questions, namely discussing machine learning methods applied in DNA storage, identifying compression techniques for DNA storage, knowing the role of deep learning in the compression process for DNA storage, knowing how generative models are associated with deep learning, knowing how generative models are applied in the compression process, and knowing latent space can be formed. The study highlights open problems that need to be solved and provides an identified research direction.

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Medical Image Compression Based on Variational Autoencoder

Cited by 12 publications

References 36 publications

Efficient MRI Image Real-Time Processing Using FPGA-Based IIR Filters

Efficient MRI Image Real-Time Processing Using FPGA-Based IIR Filters

Scaling models of visual working memory to natural images

Analysis and review of the possibility of using the generative model as a compression technique in DNA data storage: review and future research agenda

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