Multi-Sentence Complementarily Generation for Text-to-Image Synthesis

Zhao, Liang; Huang, Pingda; Chen, Tengtuo; Fu, Chunjiang; Hu, Qinghao; Zhang, Yangqianhui

doi:10.1109/tmm.2023.3297769

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…This is due to the following advantages: medical image segmentation tasks can extract specific regions for quantitative analysis and calculation, providing more objective and accurate results for diagnosis and treatment, which in turn improves patient treatment results; medical image segmentation algorithms can provide accurate references for doctors' diagnosis and treatment decisions, reducing their workload and shortening the time for diagnosis and treatment; medical image segmentation topics are computer medical image segmentation is a hot topic in the field of computer vision technology, and the study of medical image segmentation algorithms can promote the development and application of artificial intelligence technology in the field of medicine and provide reference and reference for the application in other fields. Machine learning uses algorithmic models that allow computers to learn the context of visual data on their own ( 6 ).…”

Section: Introductionmentioning

confidence: 99%

SW-UNet: a U-Net fusing sliding window transformer block with CNN for segmentation of lung nodules

Ma,

Yuan,

Guo

et al. 2023

Front. Med.

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Medical images are information carriers that visually reflect and record the anatomical structure of the human body, and play an important role in clinical diagnosis, teaching and research, etc. Modern medicine has become increasingly inseparable from the intelligent processing of medical images. In recent years, there have been more and more attempts to apply deep learning theory to medical image segmentation tasks, and it is imperative to explore a simple and efficient deep learning algorithm for medical image segmentation. In this paper, we investigate the segmentation of lung nodule images. We address the above-mentioned problems of medical image segmentation algorithms and conduct research on medical image fusion algorithms based on a hybrid channel-space attention mechanism and medical image segmentation algorithms with a hybrid architecture of Convolutional Neural Networks (CNN) and Visual Transformer. To the problem that medical image segmentation algorithms are difficult to capture long-range feature dependencies, this paper proposes a medical image segmentation model SW-UNet based on a hybrid CNN and Vision Transformer (ViT) framework. Self-attention mechanism and sliding window design of Visual Transformer are used to capture global feature associations and break the perceptual field limitation of convolutional operations due to inductive bias. At the same time, a widened self-attentive vector is used to streamline the number of modules and compress the model size so as to fit the characteristics of a small amount of medical data, which makes the model easy to be overfitted. Experiments on the LUNA16 lung nodule image dataset validate the algorithm and show that the proposed network can achieve efficient medical image segmentation on a lightweight scale. In addition, to validate the migratability of the model, we performed additional validation on other tumor datasets with desirable results. Our research addresses the crucial need for improved medical image segmentation algorithms. By introducing the SW-UNet model, which combines CNN and ViT, we successfully capture long-range feature dependencies and break the perceptual field limitations of traditional convolutional operations. This approach not only enhances the efficiency of medical image segmentation but also maintains model scalability and adaptability to small medical datasets. The positive outcomes on various tumor datasets emphasize the potential migratability and broad applicability of our proposed model in the field of medical image analysis.

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Section: Introductionmentioning

confidence: 99%