Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent automation.It is well known that single 2D CT slices lack spatial-temporal cha...Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent automation.It is well known that single 2D CT slices lack spatial-temporal characteristics and contextual information compared to 3D CT blocks.However,3D CT blocks necessitate significantly higher hardware resources during the learning phase.Therefore,efficiently exploiting temporal correlation and spatial-temporal features of 2D CT slices is crucial for ULD tasks.In this paper,we propose a ULD network with the enhanced temporal correlation for this purpose,named TCE-Net.The designed TCE module is applied to enrich the discriminate feature representation of multiple sequential CT slices.Besides,we employ multi-scale feature maps to facilitate the localization and detection of lesions in various sizes.Extensive experiments are conducted on the DeepLesion benchmark demonstrate that thismethod achieves 66.84%and 78.18%for FS@0.5 and FS@1.0,respectively,outperforming compared state-of-the-art methods.展开更多
Electrocardiogram(ECG)biometric recognition has gained considerable attention,and various methods have been proposed to facilitate its development.However,one limitation is that the diversity of ECG signals affects th...Electrocardiogram(ECG)biometric recognition has gained considerable attention,and various methods have been proposed to facilitate its development.However,one limitation is that the diversity of ECG signals affects the recognition performance.To address this issue,in this paper,we propose a novel ECG biometrics framework based on enhanced correlation and semantic-rich embedding.Firstly,we construct an enhanced correlation between the base feature and latent representation by using only one projection.Secondly,to fully exploit the semantic information,we take both the label and pairwise similarity into consideration to reduce the influence of ECG sample diversity.Furthermore,to solve the objective function,we propose an effective and efficient algorithm for optimization.Finally,extensive experiments are conducted on two benchmark datasets,and the experimental results show the effectiveness of our framework.展开更多
基金Taishan Young Scholars Program of Shandong Province,Key Development Program for Basic Research of Shandong Province(ZR2020ZD44).
文摘Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent automation.It is well known that single 2D CT slices lack spatial-temporal characteristics and contextual information compared to 3D CT blocks.However,3D CT blocks necessitate significantly higher hardware resources during the learning phase.Therefore,efficiently exploiting temporal correlation and spatial-temporal features of 2D CT slices is crucial for ULD tasks.In this paper,we propose a ULD network with the enhanced temporal correlation for this purpose,named TCE-Net.The designed TCE module is applied to enrich the discriminate feature representation of multiple sequential CT slices.Besides,we employ multi-scale feature maps to facilitate the localization and detection of lesions in various sizes.Extensive experiments are conducted on the DeepLesion benchmark demonstrate that thismethod achieves 66.84%and 78.18%for FS@0.5 and FS@1.0,respectively,outperforming compared state-of-the-art methods.
基金supported by National Natural Science Foundation of China(No.62076151)Natural Science Foundation of Shandong Province,China(No.ZR2020 MF052)the NSFC-Xinjiang Joint Fund,China(No.U1903127)。
文摘Electrocardiogram(ECG)biometric recognition has gained considerable attention,and various methods have been proposed to facilitate its development.However,one limitation is that the diversity of ECG signals affects the recognition performance.To address this issue,in this paper,we propose a novel ECG biometrics framework based on enhanced correlation and semantic-rich embedding.Firstly,we construct an enhanced correlation between the base feature and latent representation by using only one projection.Secondly,to fully exploit the semantic information,we take both the label and pairwise similarity into consideration to reduce the influence of ECG sample diversity.Furthermore,to solve the objective function,we propose an effective and efficient algorithm for optimization.Finally,extensive experiments are conducted on two benchmark datasets,and the experimental results show the effectiveness of our framework.
