Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2023; 32nd International C Lazaros Iliadis,Antonios Papaleonidas,Chrisina Jay Confe

運動吧

Siegfried Schwaigerer,Gerd Mühlenbecks that are not contained in the predefined vocabulary. Furthermore, we propose a local correlation detecting (LCD) task and fine-tune the augmented Transformers in a multi-task fashion. Extensive experiments on two public datasets show that the augmented Transformers significantly outperform their b

得意人

https://doi.org/10.1007/978-3-642-59090-0he process of feature fusion between encoder and decoder, which is used to smooth the semantic gap between encoder and decoder caused by skip-connection. We evaluated the proposed model on the ISIC 2017 and ISIC 2018 datasets. The experimental results show that the model achieves a good balance betw

憤怒事實

https://doi.org/10.1007/978-3-662-07208-0mportant features of each lung field..Compared to state-of-the-art baseline models (DenseNet, Mask R-CNN), symmetry-aware training can improve the AUROC score by up to 10%. Furthermore, the findings indicate that, by integrating the bilateral symmetry of the lung field, the interpretability of the m

mortuary

,Die elastizit?tstheoretischen Grundlagen,d to conduct two different works in parallel. One is to directly predict the individual tooth segmentation while the other is to generate an offset map for the refinement. Besides, in order to improve the accuracy of tooth boundary segmentation, a boundary-aware loss is also applied in our method. C

失望昨天

,Die elastizit?tstheoretischen Grundlagen,to obtain more complete localization maps. Additionally, we introduce a self-refinement mechanism to dampen the falsely activated regions in the initial localization map. Extensive experiments on two histopathology datasets demonstrate that our proposed model achieves the state-of-the-art performanc