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...
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...
allows us to directly apply our model to full-resolution volumes, avoiding artifacts caused by slice- or patch-wise data processing. While this work focuses on a specific application, the presented method can be applied to all kinds of paired image-to-image translation problems, such as CT↔...
1) Framework of self-supervision guided optimization: We propose S2-Net, a general framework aims to make the detect-and-describe methods suitable for cross-modality image matching. To train the basic framework, relevant constraints for single modality images are proposed. However, the lack of str...
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...
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: 对于图像区域特征,使用自下而上的特征,其表示如下式所示: ...
This paper focuses on the area of RGB(visible)-NIR(near-infrared) cross-modality image registration, which is crucial for many downstream vision tasks to fully leverage the complementary information present in visible and infrared images. In this field, researchers face two primary challenges - the...
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...
Code for "MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Training", Arxiv 2025. - mfkiwl/MatchAnything