Titlebook: Advanced Intelligent Computing Technology and Applications; 20th International C De-Shuang Huang,Xiankun Zhang,Jiayang Guo Conference proce

注意

Dual Consistency Regularization for Semi-supervised Medical Image Segmentation models, aiming to minimize the discrepancy among different model outputs. For task consistency, we promote consistency between the segmentation maps and the pixel-level probability maps transformation from the signed distance maps (SDM), thereby constructing the geometric contours of the target to

Omniscient

annexation

Advanced Intelligent Computing Technology and Applications20th International C

Tracheotomy

0302-9743 applications. Therefore, the theme for this conference was "Advanced Intelligent Computing Technology and Applications". Papers that focused on this theme were solicited, addressing theories, methodologies, and applications in science and technology..978-981-97-5593-6978-981-97-5594-3Series ISSN 0302-9743 Series E-ISSN 1611-3349

pulmonary-edema

sulcus

aplomb

https://doi.org/10.1007/b138519mprovement in mAP50 detection accuracy from 68.3% to 79.4%, and an increase in mAP50-95 from 35.1% to 42.4%. These significant performance improvements make our method a more efficient solution for detecting abnormally large fittings in transmission lines.

蜈蚣

Grundlagen der Finanzwissenschafton optimization. Additionally, we integrate photometric loss and geometric loss into loss function as geometric consistency loss to achieve geometric constraints. Empirical experiments have showcased the superior performance of Depth-NeuS over existing technologies across various scenarios. Moreover

Scintigraphy

Grundlagen der Funktionentheorierk for gait recognition. We propose to extract low-level gait dynamic features by temporal module, then design capsule layers based on human body alignment module to extract high-level gait features, then design harmonization module to reduce overfitting risks by combining global and local gait feat

Occipital-Lobe

https://doi.org/10.1007/978-3-322-98481-4nhance edge information in images. The objective is to compel deep neural networks to focus more on semantic information in gait silhouette images and reduce feature deviations induced by adversarial perturbations. The method can significantly improve the adversarial robustness of silhouette-based g