Nuclearmagnetic resonance imaging of breasts often presents complex *** tumors exhibit varying sizes,uneven intensity,and indistinct *** characteristics can lead to challenges such as low accuracy and incorrect segmen...
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Nuclearmagnetic resonance imaging of breasts often presents complex *** tumors exhibit varying sizes,uneven intensity,and indistinct *** characteristics can lead to challenges such as low accuracy and incorrect segmentation during tumor ***,we propose a two-stage breast tumor segmentation method leveraging multi-scale features and boundary attention ***,the breast region of interest is extracted to isolate the breast area from surrounding tissues and ***,we devise a fusion network incorporatingmulti-scale features and boundary attentionmechanisms for breast tumor *** incorporate multi-scale parallel dilated convolution modules into the network,enhancing its capability to segment tumors of various sizes through multi-scale convolution and novel fusion ***,attention and boundary detection modules are included to augment the network’s capacity to locate tumors by capturing nonlocal dependencies in both spatial and channel ***,a hybrid loss function with boundary weight is employed to address sample class imbalance issues and enhance the network’s boundary maintenance capability through additional *** was evaluated using breast data from 207 patients at RuijinHospital,resulting in a 6.64%increase in Dice similarity coefficient compared to the *** results demonstrate the superiority of the method over other segmentation techniques,with fewer model parameters.
The purpose of this paper is to explore the feasibility and effectiveness of utilizing Vector Autoregression (VAR) model to predict the moisture content of construction materials. The moisture content of construction ...
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The classification of short-term power load data by clustering algorithm can lay a good foundation for the subsequent power load forecasting work and provide a more efficient, safe and reliable direction for the opera...
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Research on StarCraft II (SC2) is considered important due to its similarity to real-life tasks and its potential to inspire game artificial intelligence design. However, the complexity of SC2 presents considerable ch...
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Research on StarCraft II (SC2) is considered important due to its similarity to real-life tasks and its potential to inspire game artificial intelligence design. However, the complexity of SC2 presents considerable challenges. In 2019, DeepMind proposed AlphaStar (AS), an agent that achieved Grandmaster level in SC2. Nevertheless, the reasons for AS's success remain unclear. In this article, we revisit AS by analyzing its technical details, implementation codes, and replays. We also propose the open-sourced mini-scaled AS's new versions to do ablation studies. We classify SC2 problems by difficulty level and suggest a research path for tackling them. We then identify several limitations of AS, such as its lack of strategic view, reasoning, scouting, changes in tactics, and planning. Our article also presents the first analysis of AS's replays. In conclusion, we emphasize that there is still a long way to solve the final SC2 problem.
Brain medical image registration is a fundamental premise for the computer-assisted treatment of brain diseases. The brain is one of the most important and complex organs of the human body, and it is very challenging ...
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Brain medical image registration is a fundamental premise for the computer-assisted treatment of brain diseases. The brain is one of the most important and complex organs of the human body, and it is very challenging to perform accurate and fast registration on it. Aiming at the problem of voxel folding in the deformation field and low registration accuracy when facing complex and fine objects, this paper proposed a fully convolutional multi-constraint cascaded attention network (MCANet). The network is composed of two registration sub-network cascades and performs coarse-to-fine registration of input image pairs in an iterative manner. The registration subnetwork is called the dilated self-attention network (DSNet), which incorporates dilated convolution combinations with different dilation rates and attention gate modules. During the training of MCANet, a double regularization constraint was applied to punish, in a targeted manner, the excessive deformation problem, so that the network can generate relatively smooth deformation while having high registration accuracy. Experimental results on the Mindboggle101 dataset showed that the registration accuracy of MCANet was significantly better than several existing advanced registration methods, and the network can complete relatively smooth registration.
Spatiotemporal prediction can serve a series of real-world applications such as urban transportation planning, public risk assessment, and air pollution control. However, this work is considered challenging due to the...
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Due to its powerful capabilities in natural language understanding and content generation, ChatGPT has received widespread attention since its launch in 2022. An increasing number of ChatGPT-related projects (that enh...
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Due to its powerful capabilities in natural language understanding and content generation, ChatGPT has received widespread attention since its launch in 2022. An increasing number of ChatGPT-related projects (that enhance the capabilities of ChatGPT, develop applications by calling ChatGPT APIs, etc.) are being released on GitHub and have sparked widespread discussions. However, GitHub does not provide a detailed classification of these projects to help users effectively explore interested projects. Additionally, the issues raised by users for these projects cover various aspects, e.g., installation, usage, and updates. It would be valuable to help developers prioritize more urgent issues and improve development efficiency. Unfortunately, there is currently no research focused on understanding the categories and issues of ChatGPT-related projects. To fill this gap, we retrieved 71,244 projects from GitHub using the keyword 'ChatGPT' and selected the top 200 representative projects with the highest numbers of stars as our dataset. By analyzing the project descriptions, we identified three primary categories of ChatGPT-related projects, namely ChatGPT Implementation & Training, ChatGPT Application, ChatGPT Improvement & Extension. We further built a classifier for automatically categorizing projects based on the 200 manually annotated projects. Next, we applied a topic modeling technique to 23,609 issues of those projects and identified ten issue topics, e.g., model reply and interaction interface. We analyzed the popularity, difficulty, and evolution of each issue topic within the three project categories and further proposed a method for recommending solutions for open issues by summarizing the pull requests associated with closed issues. Our main findings are: (1) The increase in the number of projects within the three categories is closely related to the development of ChatGPT;and (2) There are significant differences in the popularity, difficulty, and evolutionar
Multiobjective evolutionary multitasking (MOEMT) has become very popular in recent years, as this kind of methods aims to solve a set of multiobjective optimization problems (MOPs) simultaneously, which has been valid...
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Aerial image object detection has increasingly become a popular research direction within the field of small object detection. Unlike natural scene images, objects in aerial images occupy a very small pixel area, maki...
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Aerial image object detection has increasingly become a popular research direction within the field of small object detection. Unlike natural scene images, objects in aerial images occupy a very small pixel area, making them easily susceptible to background noise interference. In addition, most aerial images are taken from an overhead perspective, providing limited and highly similar information from the tops of objects, which poses significant challenges for object localization and classification. To address these issues, this paper proposes a more efficient aerial image object detection network, which is mainly composed of a Differential Information Injection Network (DIIN), a Multi-scale Dilated Object Perception Module (MDOP), and a Cross-attention Feature Fusion Module (CFFM). First, the Difference Information Injection Network is designed to capture asymmetric information in features. Through semantic information interaction and spatial information perception across spatial and channel dimensions, it can effectively achieve cross-feature difference information injection. Next, the Multi-scale Dilated Object Perception module is employed, which uses parallel dilated convolutions to integrate feature context from different receptive fields, thereby enhancing the model's ability to perceive small objects. Finally, to efficiently fuse features at different levels, a cross-attention-based feature fusion module is proposed, which adaptively merges the detailed and semantic information between features, strengthening their representation capability and ultimately improving detection performance. To validate the effectiveness of our method, we conducted extensive experimental evaluations on the challenging VisDrone2019 dataset. The experimental results show that compared with other mainstream models, our proposed method achieves better detection performance in aerial image object detection tasks. At the same time, a balance between computational efficiency and detecti
Bioinformatics workloads differ significantly from traditional scientific computing and AI workloads because they consist primarily of integer-only operations and string comparisons rather than floating-point operatio...
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