This need is even more acute as it is becoming increasingly important to handle images from multiple modalities in clinical practice.We present a model-based segmentation framework in which a geometric model of the heart is automatically adapted to medical images. The adaptation starts with the ...
CLIP Encoder + Decoder:从CLIP Visual Transformer 中三个layer 接出来feature Image-Text Interpolation 对于分割目标信息 用CLIP 提取 image 和text embedding,之后 进行加权得到conditional vector :xi=asi+(1−a)ti conditional vector的使用方法: we modulate the decoder’s input activation by a conditional v...
contribution: 1.作者提出了Multi-Modal Mutual Attention(M^3Att) 和Multi-Model Mutual Decoder(M^3Dec) 以实现多模态信息的处理和融合,并在此基础上搭建了referring segmentation框架; 2.作者提出了Iterative Multi-Modal Interaction (IMI) 和 Language Feature Reconstruction (LFR)模块以实现深度多模态交互; 3.在...
Multi-modality MRIs, which offer diverse feature information, are commonly utilized in brain tumor image segmentation. Deep neural networks have become prevalent in this field; however, many approaches simply concatenate different modalities and input them directly into the neural network for segmentation...
Probabilistic logicNowadays, multi-source image acquisition attracts an increasing interest in many fields such as multi-modal medical image segmentation. Such ... J Lapuyade-Lahorgue,JH Xue,R Su - 《IEEE Transactions on Image Processing》 被引量: 0发表: 2017年 加载更多研究...
Neonatal brain segmentation in magnetic resonance (MR) is a challenging problem due to poor image quality and similar levels of intensity between white and gray matter in MR-T1 and T2 images. To tackle this problem, most existing approaches are based on multi-atlas label fusion strategies, which...
20240911【医疗人工智能的前沿进展】马骏:Towards Biomedical Image Segmentation Foundation Models 2040 1 34:55 App 20210915【兼听则明:多源异构数据协同技术】朱磊:Multi-modal Hash Representation Learning 3232 -- 29:19 App 20230531【大模型时代下的三维视觉:路在何方?】杨波:3D Semantic and Instance Segmentati...
Further, motivated by the recent success in applying deep learning for natural image analysis, we implement the three image fusion schemes above based on the Convolutional Neural Network (CNN) with varied structures, and combined into a single framework. The proposed image segmentation framework is ...
Multi-modal image segmentation using a modified Hopfield neural network In most computer vision applications, it is required to segment objects from a background. In case of a muti-modal image the segmentation is an involved pr... S Rout,Seethalakshmy,P Srivastava,... - 《Pattern Recognition》...
The segmentation result \overline{o}_i obtained by the segment-level model T is not good enough, since it only considers the local information over w_t shots while ignoring the global contextual information over the whole movie. In order to capture the global structure, we develop a global ...