CrossMoDA 2023 (Cross-Modality Domain Adaptation) 是目前医疗影像领域中最大的跨模态域适应数据集,它专注于分割涉及前庭神经瘤(Vestibular Schwannoma, VS)随访和治疗计划中的关键结构:肿瘤和耳蜗。自 2021 年首次举办以来,该挑战赛已经成功举办了三届,本次介绍的是最新的 CrossMoDA 2023 数据集。与前两届 (cros...
Adversarial Networks (GAN) based bidirectional cross-modality unsupervised domain adaptation (GBCUDA) framework is developed for cardiac image segmentation, which can effectively tackle the problem of network's segmentation performance degradation when adapting to the target domain without ground truth labels...
Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image,程序员大本营,技术文章内容聚合第一站。
Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss, IJCAI, pp. 691-697, 2018. (https://arxiv.org/abs/1804.10916) (short version) Introduction Deep convolutional networks have demonstrated the state-of-the-art performance on various medic...
Paper tables with annotated results for Learning Site-specific Styles for Multi-institutional Unsupervised Cross-modality Domain Adaptation
wecapitalizeonmulti-modalitytoaddressUnsupervised 1 a r X i v : 1 9 1 1 . 1 2 6 7 6 v 1 [ c s . C V ] 2 8 N o v 2 0 1 9 Domain Adaptation (UDA)? We coin our method Cross-Modal UDA, ‘xMUDA’ in short, and consider 3 real-to-real adaptation scenarios with different...
(2018). Unsupervised cross-modality domain adaptation of convnets for biomedical image segmentations with adversarial loss. arXiv preprint arXiv:1804.10916 Zhu, J.-Y., Park, T., Isola, P., Efros, A.A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. ...
Such cross-modality domain shifts can significantly degrade the performance of deep neural networks in medical imaging if not properly addressed [5], [6], [7]. Unsupervised domain adaptation [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18] was developed to ...
MICCAI2019 多模分割相关论文笔记 Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation 由于来自域转移,对来自某个源域的标记数据进行训练的深度学习模型,通常在来自不同目标域的数据上表现较差。无监督域自适应方法通过减轻...论文...
Cross-modal few-shot adaptation with CLIP. Contribute to linzhiqiu/cross_modal_adaptation development by creating an account on GitHub.