Online continual learning (OCL) requires the models to learn from constant, endless streams of data. While significant efforts have been made in this field, most were focused on mitigating the catastrophic forgetting ...
Neural Collapse (NC) presents an elegant geometric structure that enables individual activations (features), class means and classifier (weights) vectors to reach optimal interclass separability during the terminal ph...
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Cone-beam computed tomography (CBCT) is widely used in clinical diagnosis of vertical root fractures (VRFs) which presents as crack on the teeth. However, manually checking the VRFs from a larger number of CBCT images...
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Background Brain network describing interconnections between brain regions contains abundant topological *** is a challenge for the existing statistical methods(e.g.,t test)to investigate the topological differences o...
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Background Brain network describing interconnections between brain regions contains abundant topological *** is a challenge for the existing statistical methods(e.g.,t test)to investigate the topological differences of brain *** We proposed a kernel based statistic framework for identifying topological differences in brain *** our framework,the topological similarities between paired brain networks were measured by graph ***,graph kernels are embedded into maximum mean discrepancy for calculating kernel based test *** on this test statistic,we adopted conditional Monte Carlo simulation to compute the statistical significance(i.e.,P value)and statistical *** recruited 33 patients with Alzheimer’s disease(AD),33 patients with early mild cognitive impairment(EMCI),33 patients with late mild cognitive impairment(LMCI)and 33 normal controls(NC)in our *** are no statistical differences in demographic information between patients and *** compared state-of-the-art statistical methods include t test,t squared test,two-sample permutation test and non-normal *** We applied the proposed shortest path matched kernel to our framework for investigating the statistical differences of shortest path topological structures in brain networks of AD and *** compared our method with the existing state-of-the-art statistical methods in brain network characteristic including clustering coefficient and functional connection among EMCI,LMCI,AD,and *** results indicate that our framework can capture the statistically discriminative shortest path topological structures,such as shortest path from right rolandic operculum to right supplementary motor area(P=0.00314,statistical power=0.803).In clustering coefficient and functional connection,our framework outperforms the state-of-the-art statistical methods,such as P=0.0013 and statistical power=0.83 in the analysis of AD and *** Our proposed kernel b
Physics-informed neural networks (PINNs) have shown promising potential for solving partial differential equations (PDEs) using deep learning. However, PINNs face training difficulties for evolutionary PDEs, particula...
The possibility of axillary lymph node metastasis differs in different breast cancer patients and is the strongest prognostic indicator in breast cancer. The existing studies mainly explored the relationship of axilla...
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Synthesizing Aβ-PET images from cross-modal neuroimaging for diagnosing Alzheimer's disease through multi-modal medical image fusion is highly significant. However, there are relatively few studies in this area. ...
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Extensive research has been conducted in recent years to solve the long-tailed distribution and achieved excellent results. However, in contrast to well-designed data, datasets with label noise are common in the real ...
Extensive research has been conducted in recent years to solve the long-tailed distribution and achieved excellent results. However, in contrast to well-designed data, datasets with label noise are common in the real world, even in long-tailed datasets. Then, loss functions that rely on prior knowledge of correct labels for long-tailed distributions will fail. To solve the above problems, the robustness of different loss functions in long-tailed data containing noise is first analyzed. Then algorithmic improvements are made to the LDAM loss, which is focused on dealing with long-tailed problems. We propose a robust loss function DRL, which solves the noisy long-tailed problem with two regularisation terms: label regularisation and sample regularisation. Experiments on several datasets validate the effectiveness of the proposed loss function.
Imbalanced multi-label image classification has gained increasing attention recently, in which each sample has multiple class labels, but the number of each category is unevenly distributed. It’s common in practical ...
Imbalanced multi-label image classification has gained increasing attention recently, in which each sample has multiple class labels, but the number of each category is unevenly distributed. It’s common in practical applications but traditional multi-label learning methods can hardly deal with imbalance problems. In this paper, we propose an effective method to tackle imbalanced multi-label learning. The class-aware embedding network is proposed to learn robust class-based representation. Additionally, by using the distribution-balanced loss to weigh different samples, our model can improve the feature learning ability of minority classes. Extensive experiments on widely used long-tailed manual multi-label datasets like VOC-LT and COCO-LT explicitly validate the proposed good method.
Training only one deep model for large-scale cross-scene video foreground segmentation is challenging due to the off-the-shelf deep learning based segmentor relies on scene-specific structural information. This result...
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