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...
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 forSegmenting Vestibular Schwannoma andCochlea withData Augmentation andModel Ensembledoi:10.1007/978-3-031-09002-8_45Magnetic resonance images (MRIs) are widely used to quantify the volume of the vestibular schwannoma (VS) and cochlea. Recently, deep ...
随着CycleGAN在未配对图像到图像转换方面的成功,许多以前的image adaptation works都是基于改进的CycleGAN,并应用在自然数据集(Russo et al.2017;Hoffman等人,2018)和医学图像分割(Hoo等人,2018;张等人,2018b;Chen等人,2018年) feature-level adaptation methods(通过提取域不变特征) 以前的工作通过最小化domain statisti...
Modality Classifier解决的只是Modality-invariant问题,Cross-Modal similarity与Semantically-discriminative就需要Feature Projector(FP)来解决了,FP分为两部分,分别想解决这两个问题: Label prediction:Semantically-discriminative Structure preservation:Cross-Modal similarity 其中,Label prediction的目的在于使映射出的不同语义...
Domain adaptation aims at addressing this gap, but existing work concerns mostly 2D semantic segmentation [11, 16, 28, 34] and rarely 3D [32]. We also observe that previous domain adaptation work fo- cuses on single modality, whereas 3D datasets are of...
The results are not ideal when the domain gap is large. To solve the problem of lacking supervision, we introduce masked modeling into this task and propose a method Mx2M, which utilizes masked cross-modality modeling to reduce the large domain gap. Our Mx2M contains two components. One is...
A novel Generative 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...
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...
Domain adaptation is an important task to enable learning when labels are scarce. While most works focus only on the image modality, there are many important multi-modal datasets. In order to leverage multi-modality for domain adaptation, we propose cross-modal learning, where we enforce ...