HIGSA: Human image generation with self-attention

Wu, Hailei; He, Fazhi; Si, Tongzhen; Duan, Yun‐Bo; Yan, Xiaohu

doi:10.1016/j.aei.2022.101856

Cited by 3 publications

(1 citation statement)

References 56 publications

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“…In image segmentation tasks, it can be used to guide the model to better capture the contours and details of the target object. In addition, the attention mechanism is widely used in image generation tasks to produce images with more detail and realism [53,54].…”

Section: Attention Mechanismmentioning

confidence: 99%

An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation

Jian,

Song,

Zhang

et al. 2024

Sensors

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Fluorescence in situ hybridization (FISH) is a powerful cytogenetic method used to precisely detect and localize nucleic acid sequences. This technique is proving to be an invaluable tool in medical diagnostics and has made significant contributions to biology and the life sciences. However, the number of cells is large and the nucleic acid sequences are disorganized in the FISH images taken using the microscope. Processing and analyzing images is a time-consuming and laborious task for researchers, as it can easily tire the human eyes and lead to errors in judgment. In recent years, deep learning has made significant progress in the field of medical imaging, especially the successful application of introducing the attention mechanism. The attention mechanism, as a key component of deep learning, improves the understanding and interpretation of medical images by giving different weights to different regions of the image, enabling the model to focus more on important features. To address the challenges in FISH image analysis, we combined medical imaging with deep learning to develop the SEAM-Unet++ automated cell contour segmentation algorithm with integrated attention mechanism. The significant advantage of this algorithm is that it improves the accuracy of cell contours in FISH images. Experiments have demonstrated that by introducing the attention mechanism, our method is able to segment cells that are adherent to each other more efficiently.

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Section: Attention Mechanismmentioning

confidence: 99%

An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation

Jian,

Song,

Zhang

et al. 2024

Sensors

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Indicative Vision Transformer for end-to-end zero-shot sketch-based image retrieval

Zhang,

Cheng,

Kou

et al. 2024

Advanced Engineering Informatics

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Synthetic Image Generation Using Deep Learning: A Systematic Literature Review

Zulfiqar,

Muhammad Daudpota,

Shariq Imran

et al. 2024

Computational Intelligence

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The advent of deep neural networks and improved computational power have brought a revolutionary transformation in the fields of computer vision and image processing. Within the realm of computer vision, there has been a significant interest in the area of synthetic image generation, which is a creative side of AI. Many researchers have introduced innovative methods to identify deep neural network‐based architectures involved in image generation via different modes of input, like text, scene graph layouts and so forth to generate synthetic images. Computer‐generated images have been found to contribute a lot to the training of different machine and deep‐learning models. Nonetheless, we have observed an immediate need for a comprehensive and systematic literature review that encompasses a summary and critical evaluation of current primary studies' approaches toward image generation. To address this, we carried out a systematic literature review on synthetic image generation approaches published from 2018 to February 2023. Moreover, we have conducted a systematic review of various datasets, approaches to image generation, performance metrics for existing methods, and a brief experimental comparison of DCGAN (deep convolutional generative adversarial network) and cGAN (conditional generative adversarial network) in the context of image generation. Additionally, we have identified applications related to image generation models with critical evaluation of the primary studies on the subject matter. Finally, we present some future research directions to further contribute to the field of image generation using deep neural networks.

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HIGSA: Human image generation with self-attention

Cited by 3 publications

References 56 publications

An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation

An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation

Indicative Vision Transformer for end-to-end zero-shot sketch-based image retrieval

Synthetic Image Generation Using Deep Learning: A Systematic Literature Review

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