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Cancer, a global health threat, demands effective diagnostic solutions to combat its impact on public health, particularly for breast, colon, and lung cancers. Early and accurate diagnosis is essential for successful treatment, prompting the rise ...

• The introduction of ConvMergeNet to optimize feature extraction for improved model performance.
• The integration of ConvMergeNet with ResBoost, achieving gooddiagnostic precision and robust performance in complex settings.
• Achieves ...

With the rapid development of technology, smart environments utilizing the Internet of Things, artificial intelligence, and big data are improving the quality of life and work efficiency through connected devices. However, these advances present ...

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Target occlusion or pollution has always been a common and difficult problem in 3D reconstruction, seriously affecting the reconstruction effect, especially in single image scenario. To address the issues of incomplete reconstruction caused by ...

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• This paper proposes a novel framework for 3D face reconstruction with missing pixel completion, which enables the generation of complete 3D face models from largely masked images. Unlike previous reconstruction methods, it possesses the ...

To address the limitation of preserving data for dynamic visualization in fetal ultrasound screening, a novel framework is proposed to facilitate the generation of fetal four-chamber echocardiogram videos, incorporating multi-source visual fusion ...

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• A frame interpolation is used to synthesis videos via inter-frame pixel movement.
• A synchronizer is developed to regulate videos with actual cardiac rhythm fusion.
• The synchronizer aligns images with blood flow to synchronize the ...

Liver disease diagnosis is pivotal for effective patient management, and machine learning techniques have shown promise in this domain. In this study, we investigate the impact of Polynomial-SHapley Additive exPlanations analysis on enhancing the ...

• The polynomial-SHAP approach shows exceptional effectiveness in capturing feature importance and interactions.
• The proposed approach provides outstanding diagnostic support and enables a personalized management strategy.
• Effective ...

In the Metaverse, 3D modeling techniques and autoencoders offer a novel approach for handling 2D portraits of Non-Fungible Tokens (NFTs). These techniques have significant applications in the metaverse, a virtual, shared, and persistently online ...

• We proposed an autoencoder method for 3D reconstruction of 2D NFT portraits in the metaverse, inferring 3D structure and texture.
• We designed techniques for consistent 3D NFT portraits under various lighting conditions, enhancing their ...

The Text to Audible-Video Generation (TAVG) task involves generating videos with accompanying audio based on text descriptions. Achieving this requires skillful alignment of both audio and video elements. To support research in this field, we have ...

Deformable medical image registration is an important precursor task for surgical automation, while enhancing the registration performance of 2D medical images remains a challenging work. Existing methods primarily minimize the similarity loss ...

• C2FResMorph is a two-stage framework for accurate 2D medical image registration.
• A ResMorph registration network effectively improves registration performance.
• Residual structured loss enhances generalization for 2D image ...

In image segmentation, effective upsampling plays a pivotal role in recovering lost spatial information during the process of downsampling. Standard skip connections designed to mitigate this and prevalent in most models, often fall short of ...

The UNet architecture, which is widely used for biomedical image segmentation, has limitations like blurred feature maps and over- or under-segmented regions. To overcome these limitations, we propose a novel network architecture called MACCoM (...

• Proposal of Multi-Scope Attention Modules (MSAM) for diverse features capturing in segmentation networks.
• Proposal of Spatial Multi-head Attention (SMA) for optimized inter-layer communication.
• For fine deep feature extraction, ...

The traditional evaluation of information retrieval (IR) systems is generally very costly as it requires manual relevance annotation from human experts. Recent advancements in generative artificial intelligence -specifically large language models (LLMs)- ...

Due to the high cost of pixel-level labels required for fully-supervised semantic segmentation, weakly-supervised segmentation has emerged as a more viable option recently. Existing weakly-supervised methods tried to generate pseudo-labels without pixel-...

We present Lightning Attention, the first linear attention implementation that maintains a constant training speed for various sequence lengths under fixed memory consumption. Due to the issue with cumulative summation operations (cumsum), previous ...

Pre-trained large language models (LLMs) need fine-tuning to improve their responsiveness to natural language instructions. Federated learning offers a way to fine-tune LLMs using the abundant data on end devices without compromising data privacy. Most ...

Spectrum map is a database that stores multidimensional representations of spectrum situation information. It provides support for spectrum sensing and endows wireless communication networks with intelligence. However, the ubiquitous deployment ...

This paper aims to infer fine‐grained spectrum situation of the target region based on coarse‐grained observation. It adopts the idea of super resolution and proposes an inference framework based on deep residual network. image image

Query expansion has been widely used to improve the search results of first-stage retrievers, yet its influence on second-stage, cross-encoder rankers remains under-explored. A recent study shows that current expansion techniques benefit weaker models ...

The reconstruction of quantum states from experimental measurements, often achieved using quantum state tomography (QST), is crucial for the verification and benchmarking of quantum devices. However, performing QST for a generic unstructured quantum state ...

This study focuses on the posture problem in the sewing process and utilizes computer vision recognition technology to identify and warn the posture status of sewing workers, thereby reducing the risks and stress during the sewing process. By ...

The Intelligent Transportation System (ITS) serves as a pivotal element within urban networks, offering decision support to users and connected automated vehicles (CAVs) through comprehensive information gathering, sensing, device control, and data ...

This paper studies the problem of joint deployment, user association, channel, and resource allocation in unmanned aerial vehicle‐enabled access network. Since different user equipments performing different tasks and have different data rate ...

This paper investigates the priority‐aware traffic fairness problem in a unmanned aerial vehicle‐enabled wireless access network. A self‐organized and distributed framework is proposed where multi‐unmanned aerial vehicle deployment, user association, ...