Search Results

In the multi-organ segmentation task of medical images, there are some challenging issues such as the complex background, blurred boundaries between organs, and the larger scale difference in volume. Due to the local receptive fields of ...

• Proposed an adjacent-scale fusion UNet to enhance the effectiveness and efficiency.
• The designs of LKRB and LKGRN help explore bridging the gap between ViTs and CNNs.
• Using Grad-CAM to gain a deeper understanding of the information ...

Structured pruning reduces the computational overhead of deep neural networks by removing redundant sub-structures. However, assessing the relative importance of different sub-structures remains a significant challenge, particularly in advanced ...

convolutional neural networks (CNNs) show great potential in medical image segmentation tasks, and can provide reliable basis for disease diagnosis and clinical research. However, CNNs exhibit general limitations on modeling explicit long-range ...

• Proposing segmentation network by adopting Transformer-guided Feature Aggregation.
• Integrating complementary features of images from CNN and Transformer branches.
• Fusing global context information by Two-stream Cascaded Feature ...

Research on image classification and text translation for comics have transpired largely independent of one another. Machine translation tools focus on comics’ text features, thereby largely ignoring comics’ heavily visual dimension. Image ...$\stackrel{}{\mathrm{}}$

Deep Convolutional Neural Networks (CNNs) have been successfully used for processing images, videos, sounds, and more generic sensor data for detecting objects, patterns, and events. In this work, we propose MEPAD, a memory-efficient parallelized ...

Resistive random-access memory (ReRAM)-based processing-in-memory (PIM) architectures are used extensively to accelerate inferencing/training with convolutional neural networks (CNNs). Three-dimensional (3D) integration is an enabling technology to ...

Medical image semantic segmentation is a crucial technique in medical imaging processing, providing essential diagnostic support by precisely delineating different tissue structures and pathological areas within an image. However, the pursuit of ...

This review paper presents an in-depth exploration of gastrointestinal (GI) tract image analysis, particularly emphasizing organ and polyp segmentation. It addresses the inherent challenges posed by the GI tract's complex anatomy and diverse ...

• Comprehensive analysis of hybrid architectures integrating CNNs and Transformers for GI tract analysis, highlighting improved context awareness and accuracy.
• Detailed evaluation of benchmark and customized datasets for GI tract image ...

Current research on image classification has combined convolutional neural networks (CNNs) and transformers to introduce inductive biases to the model, enhancing its ability to handle long-range dependencies. However, these integrated models have ...

In recent years, QUIC protocol has emerged as a promising alternative to traditional transport protocols like TCP and UDP, offering significant performance improvements in latency and throughput. Additionally, QUIC provides strong security protection ...

The aim of this study is to employ deep learning models for microstructural image classification. The microstructures provide valuable insights into material's history and properties, but manual classification is time consuming, labor-intensive and ...

Convolutional neural networks (CNNs) have made substantial contributions to the domain of plant disease diagnosis, attaining noteworthy levels of accuracy. The primary objective of this study is to enhance the capabilities of CNNs within this ...$$

Automation is increasingly at the forefront of transportation research, with the potential to bring fully autonomous vehicles to our roads in the coming years. This comprehensive survey provides a holistic look at the essential components and ...

Deep learning is having a particularly revolutionary effect in the field of artificial intelligence, namely in computer vision, where machines are equipped with the capacity to comprehend and evaluate visual information. Throughout history, convolutional ...

In recent years, there has been a growing focus on the development of compact and efficient network techniques in the computer vision research field. Towards this goal, this study presents KDVGG-Lite, an innovative image classification model that ...

Real-time object detection is significant for industrial and research fields. On edge devices, a giant model is difficult to achieve the real-time detecting requirement, and a lightweight model built from a large number of the depth-wise separable ...

• The proposed conv-simpleformer network (CS-Net) for agricultural image segmentation.
• Simple-attention block (SIAB) is designed to reduce the network's computational complexity.
• The comparison and ablation experiments verify the ...

Agricultural image segmentation needs to catch up to the development speed of deep learning, and the explosive computational overhead and limited high-quality labeled datasets are the main reasons preventing the application of Transformers to ...

In this paper, we propose a new deformable convolutional network with attention mechanism to deal with the Chinese character of various dynasties. These ancient Chinese characters are hieroglyph with special spatial structure and gradually ...

An artificially generated dorsal hand vein database called 'GenVeins' (see Beukes, 2023) is developed in this study for the purpose of acquiring sets of fictitious training and validation individuals which are large enough to represent the entire ...

Determining the script of historical manuscripts is pivotal for understanding historical narratives, providing historians with vital insights into the past. In this study, our focus lies in developing an automated system for effectively identifying the ...