Visual Intelligence, Journal Year: 2025, Volume and Issue: 3(1)
Published: Jan. 3, 2025
Language: Английский
Citations
5Transformers for Neuroimage Segmentation: Scoping Review
Journal of Medical Internet Research, Journal Year: 2025, Volume and Issue: 27, P. e57723 - e57723
Published: Jan. 29, 2025
Background Neuroimaging segmentation is increasingly important for diagnosing and planning treatments neurological diseases. Manual time-consuming, apart from being prone to human error variability. Transformers are a promising deep learning approach automated medical image segmentation. Objective This scoping review will synthesize current literature assess the use of various transformer models neuroimaging Methods A systematic search in major databases, including Scopus, IEEE Xplore, PubMed, ACM Digital Library, was carried out studies applying transformers problems 2019 through 2023. The inclusion criteria allow only peer-reviewed journal papers conference focused on transformer-based brain imaging data. Excluded dealing with nonneuroimaging data or raw signals electroencephalogram Data extraction performed identify key study details, modalities, datasets, conditions, models, evaluation metrics. Results were synthesized using narrative approach. Of 1246 publications identified, 67 (5.38%) met criteria. Half all included published 2022, more than two-thirds used segmenting tumors. most common modality magnetic resonance (n=59, 88.06%), while frequently dataset tumor (n=39, 58.21%). 3D (n=42, 62.69%) prevalent their 2D counterparts. developed those hybrid convolutional neural network-transformer architectures (n=57, 85.07%), where vision type (n=37, 55.22%). frequent metric Dice score (n=63, 94.03%). Studies generally reported increased accuracy ability model both local global features images. Conclusions represents recent increase adoption segmentation, particularly detection. Currently, achieve state-of-the-art performances benchmark datasets over standalone models. Nevertheless, applicability remains highly limited by high computational costs potential overfitting small datasets. heavy reliance field hints at diverse set validate variety Further research needed define optimal training methods clinical applications. Continuing development may make fast, accurate, reliable which could lead improved tools evaluating disorders.
Language: Английский
Citations
2Classification of Mobile-Based Oral Cancer Images Using the Vision Transformer and the Swin Transformer
Cancers, Journal Year: 2024, Volume and Issue: 16(5), P. 987 - 987
Published: Feb. 29, 2024
Oral cancer, a pervasive and rapidly growing malignant disease, poses significant global health concern. Early accurate diagnosis is pivotal for improving patient outcomes. Automatic methods based on artificial intelligence have shown promising results in the oral cancer field, but accuracy still needs to be improved realistic diagnostic scenarios. Vision Transformers (ViT) outperformed learning CNN models recently many computer vision benchmark tasks. This study explores effectiveness of Transformer Swin Transformer, two cutting-edge variants transformer architecture, mobile-based image classification application. The pre-trained model achieved 88.7% binary task, outperforming ViT by 2.3%, while conventional convolutional network VGG19 ResNet50 85.2% 84.5% accuracy. Our experiments demonstrate that these transformer-based architectures outperform traditional neural networks terms classification, underscore potential advancing state art analysis.
Language: Английский
Citations
16RGB-Angle-Wheel: A new data augmentation method for deep learning models
Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 291, P. 111615 - 111615
Published: March 5, 2024
Language: Английский
Citations
12Water body extraction from high spatial resolution remote sensing images based on enhanced U-Net and multi-scale information fusion
Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)
Published: July 12, 2024
Abstract Employing deep learning techniques for the semantic segmentation of remote sensing images has emerged as a prevalent approach acquiring information about water bodies. Yet, current models frequently fall short in accurately extracting bodies from high-resolution images, these often present intricate details terrestrial objects and complex backgrounds. Vegetation, shadows, other close to boundaries have increased similarity Moreover, different boundary complexities, shapes, sizes. This situation makes it somewhat challenging distinguish images. To overcome difficulties, this paper presents novel network model named EU-Net, specifically designed extract The proposed EU-Net model, with U-net backbone network, incorporates improved residual connections attention mechanisms, designs multi-scale dilated convolution feature fusion modules enhance body extraction performance scenarios. Specifically, are introduced enable more features; mechanism is employed improve model's discriminative ability by focusing on important channels spatial areas. implemented technique enhances receptive field while maintaining same number parameters. module capable processing both small-scale large-scale structures simultaneously modeling context relationships features at scales. Experimental results validate superior identifying outperforming terms accuracy.
Language: Английский
Citations
11An efficient frequency domain fusion network of infrared and visible images
Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108013 - 108013
Published: Feb. 5, 2024
Language: Английский
Citations
9ViTs as backbones: Leveraging vision transformers for feature extraction
Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 102951 - 102951
Published: Jan. 1, 2025
Language: Английский
Citations
1A Feature Enhancement Network Based on Image Partitioning in a Multi-Branch Encoder-Decoder Architecture
Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: 311, P. 113120 - 113120
Published: Feb. 1, 2025
Language: Английский
Citations
1A Multi-class Hybrid Variational Autoencoder and Vision Transformer Model for Enhanced Plant Disease Identification
Intelligent Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 200490 - 200490
Published: Feb. 1, 2025
Language: Английский
Citations
1Efficient and robust phase unwrapping method based on SFNet
Optics Express, Journal Year: 2024, Volume and Issue: 32(9), P. 15410 - 15410
Published: March 21, 2024
Phase unwrapping is a crucial step in obtaining the final physical information field of optical metrology. Although good at dealing with phase discontinuity and noise, most deep learning-based spatial methods suffer from complex model unsatisfactory performance, partially due to simple noise type for training datasets limited interpretability. This paper proposes highly efficient robust method based on an improved SegFormer network, SFNet. The SFNet structure uses hierarchical encoder without positional encoding decoder lightweight fully connected multilayer perceptron. proposed utilizes self-attention mechanism Transformer better capture global relationship changes reduce errors process. It has lower parameter count, speeding up unwrapping. network trained simulated dataset containing various types discontinuity. compares several state-of-the-art traditional terms important evaluation indices, such as RMSE PFS, highlighting its structural stability, robustness generalization.
Language: Английский
Citations
6