Diabetic retinopathy(DR),the main cause of irreversible blindness,is one of the most common complications of *** present,deep convolutional neural networks have achieved promising performance in automatic DR detection...
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Diabetic retinopathy(DR),the main cause of irreversible blindness,is one of the most common complications of *** present,deep convolutional neural networks have achieved promising performance in automatic DR detection *** convolution operation of methods is a local cross-correlation operation,whose receptive field de-termines the size of the local neighbourhood for ***,for retinal fundus photographs,there is not only the local information but also long-distance dependence between the lesion features(*** and exudates)scattered throughout the whole *** proposed method incorporates correlations between long-range patches into the deep learning framework to improve DR ***-wise re-lationships are used to enhance the local patch features since lesions of DR usually appear as *** Long-Range unit in the proposed network with a residual structure can be flexibly embedded into other trained *** experimental results demon-strate that the proposed approach can achieve higher accuracy than existing state-of-the-art models on Messidor and EyePACS datasets.
Efforts in weakly-supervised video anomaly detection center on detecting abnormal events within videos by coarse-grained labels, which has been successfully applied to many real-world applications. However, a signific...
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In this paper, we tackle the under-sampled MRI reconstruction problem with auxiliary contrasts. Instead of adopting a naive fusion scheme before feeding the multi-contrast features into the networks, we propose a more...
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Deep learning has been widely used in the field of mammographic image classification owing to its superiority in automatic feature ***,general deep learning models cannot achieve very satisfactory classification resul...
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Deep learning has been widely used in the field of mammographic image classification owing to its superiority in automatic feature ***,general deep learning models cannot achieve very satisfactory classification results on mammographic images because these models are not specifically designed for mammographic images and do not take the specific traits of these images into *** exploit the essential discriminant information of mammographic images,we propose a novel classification method based on a convolutional neural ***,the proposed method designs two branches to extract the discriminative features from mammographic images from the mediolateral oblique and craniocaudal(CC)mammographic *** features extracted from the two-view mammographic images contain complementary information that enables breast cancer to be more easily ***,the attention block is introduced to capture the channel-wise information by adjusting the weight of each feature map,which is beneficial to emphasising the important features of mammographic ***,we add a penalty term based on the fuzzy cluster algorithm to the cross-entropy function,which improves the generalisation ability of the classification model by maximising the interclass distance and minimising the intraclass distance of the *** experimental results on The Digital database for Screening Mammography INbreast and MIAS mammography databases illustrate that the proposed method achieves the best classification performance and is more robust than the compared state-ofthe-art classification methods.
As we all know, multi-view data is more expressive than single-view data and multi-label annotation enjoys richer supervision information than single-label, which makes multiview multi-label learning widely applicable...
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Face verification has been widely applied to identity authentication in many areas. However, due to the mask information embedded into the facial feature representation, existing face verification systems generally fa...
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Recently, 3D Gaussian Splatting (3DGS) has emerged as a prominent framework for novel view synthesis, providing high fidelity and rapid rendering speed. However, the substantial data volume of 3DGS and its attributes ...
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As a branch of clustering, multi-view clustering has received much attention in recent years. In practical applications, a common phenomenon is that partial views of some samples may be missing in the collected multi-...
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As a branch of clustering, multi-view clustering has received much attention in recent years. In practical applications, a common phenomenon is that partial views of some samples may be missing in the collected multi-view data, which poses a severe challenge to design the multi-view learning model and explore complementary and consistent information. Currently, most of the incomplete multi-view clustering methods only focus on exploring the information of available views while few works study the missing view recovery for incomplete multi-view learning. To this end, we propose an innovative diffusion-based missing view generation (DMVG) network. Moreover, for the scenarios with high missing rates, we further propose an incomplete multi-view data augmentation strategy to enhance the recovery quality for the missing views. Extensive experimental results show that the proposed DMVG can not only accurately predict missing views, but also further enhance the subsequent clustering performance in comparison with several state-of-the-art incomplete multi-view clustering methods. Copyright 2024 by the author(s)
In the field of multi-view multi-label learning, the challenges of incomplete views and missing labels are prevalent due to the complexity of manual labeling and data acquisition errors. These challenges significantly...
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In recent years, multi-view multi-label learning has aroused extensive research enthusiasm. However, multi-view multi-label data in the real world is commonly incomplete due to the uncertain factors of data collection...
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