论文题目:《Generalizable Cross-modality Medical Image Segmentation via Style Augmentation and Dual Normalization》 论文出处:CVPR2022 论文地址:论文链接 代码地址:代码链接 摘要 对于医学图像分割,想象一下,如果一个模型只在源域使用MR图像进行训练,它在目标域直接分割CT图像的性能如何?这种设置,即可泛化的跨模态分割...
Cross-modality image retrieval is particularly challenging, since images of similar (or even the same) content captured by different modalities might share few common structures. We propose a new application-independent content-based image retrieval (CBIR) system for reverse (sub-)image search across...
在交叉模态特征的引导下,语言解码器学习收集更多的相对和适当的注意特征来推断结果标题。 Image encoder 对于视觉特征的提取,本文使用了两种类型的特征。特别地,使用预训练的ResNet101来获得全局视觉特征。应用预先训练的基于RCNN的提取器来提供检测区域的自下而上的特征。 对于全局视觉特征,最终得到一个2048维的表示: ...
Cross-modality image retrieval is particularly challenging, since images of similar (or even the same) content captured by different modalities might share few common structures. We propose a new application-independent content-based image retrieval (CBIR) system for reverse (sub-)image search across...
We present a cross-modality generation framework that learns to generate translated modalities from given modalities in MR images. Our proposed method performs Image Modality Translation (abbreviated as IMT) by means of a deep learning model that leverag
MRI Image-to-Image Translation for Cross-Modality Image Registration and Segmentation Qianye Yang, Nannan Li, Zixu Zhao, Xingyu Fan, Eric Chang, Yan Xu January 2018 Download BibTex We develop a novel cross-modality generation framework that learns to genera...
Data Efficient Unsupervised Domain Adaptation For Cross-modality Image Segmentation 深度无监督域自适应(UDA)旨在仅使用未标记的目标域数据和标记的源域数据来改善目标域上的深度神经网络模型的性能。作者为多域医学图像分割引入了一种新的数据有效的 UDA 方法。所提出的方法结合了新颖的基于 VAE 的特征优先匹配和...
Cross-modality image retrieval is particularly challenging, since images of similar (or even the same) content captured by different modalities might share few common structures. We propose a new application-independent content-based image retrieval (CBIR) system for reverse (sub-)image search across...
Cross-Modality Image Translation From Brain 18F-FDG PET/CT Images to Fluid-Attenuated Inversion Recovery Images Using the CypixGAN Framework. Purpose: PET/CT and MRI can accurately diagnose dementia but are expensive and inconvenient for patients. Therefore, we aimed to generate synthetic fluid-a.....
这样,合成3D图像的冠状面和矢状面切片由来自轴平面的单独估计线形成,因此可能显示出严重的不连续性( the coronal and sagittal slices of a synthesized 3D image are formed by the separately estimated lines from the axial planes, and therefore may show strong discontinuities)。为了缓解切片不连续性问题,在...