Titlebook: Internal Migration as a Life-Course Trajectory; Concepts, Methods an Aude Bernard Book 2022 The Editor(s) (if applicable) and The Author(s)

旋转一周

mettlesome

torative learning, and finally, the pretrained encoder-decoder is associated with an adversarial encoder for final full discriminative, restorative, and adversarial learning. Our extensive experiments demonstrate that the stepwise incremental pretraining stabilizes United models training, resulting

Constituent

Aude Bernardt was required with active learning to outperform the model trained on the entire 2018 MICCAI Brain Tumor Segmentation (BraTS) dataset. Thus, active learning reduced the amount of labeled data required for image segmentation without a significant loss in the accuracy.

Lacunar-Stroke

Aude Bernard images. For segmentation followed by the SynCT generation from CycleGAN, automatic delineation is achieved through a 2.5D Residual U-Net. Quantitative evaluation demonstrates comparable segmentation results between our SynCT and radiologist drawn masks for real CT images, solving an important probl

Priapism

Wallow

缺乏

Aude Bernardo extract systematically better representations for the target domain. In particular, we address the challenge of enhancing performance on VERDICT-MRI, an advanced diffusion-weighted imaging technique, by exploiting labeled mp-MRI data. When compared to several unsupervised domain adaptation approac

别炫耀

Aude Bernarde of calibrated or under-confident models. Our extensive experiments on a large MRI database for multi-class segmentation of traumatic brain lesions shows promising results when comparing transductive with inductive predictions. We believe this study will inspire further research on transductive lea

ADAGE

Aude Bernardtation pipeline, where self-supervision is introduced to achieve further semantic alignment specifically on the disentangled content space. In the self-supervision branch, in addition to rotation prediction, we also propose elastic transformation prediction as a new pretext task. We validate our mod

BROOK

Aude Bernardin nuclei segmentation, yielding an average improvement of IoU by 0.27% and 0.11% on two tasks. Our results suggest that the UNet++. produced by the proposed .-UNet++ not only improves the segmentation accuracy slightly but also reduces the model complexity considerably.