To solve this problem, we propose a cross-modality consistency learning network, which jointly considers crossmodal learning and distillation learning. It consists of two associated components: the feature adaptation network (FANet) and the modality learning module (MLM). The FANet combines global and...
Recently, deep learning super-resolution (SR) methods have demonstrated great potential in enhancing the resolution of MRI images; however, most of them did not take the cross-modality and internal priors of MR seriously, which hinders the SR performance. In this paper, we propose a cross-...
a scalable deep learning framework that embeds data modalities into a shared low-dimensional latent space that preserves cell trajectory structures in the original datasets.scDARTis a diagonal integration method for unmatched scRNA-seq and scATAC-seq data, which is considered a more...
Contrastive learning [33] is a new technique to automatically learn generalized data representations by distinguishing similar and dissimilar data in an embedded space, which has been well developed in a variety of vision and language tasks, such as visual recognition [34,35], video-text retrieval...
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
Action-vision-language model Crossmodality Robot learning 1. Introduction Recently, large language models (LLMs) have gained popularity as versatile and powerful approaches to general purpose language processing. Being merely trained on large text corpora, they are capable of a wide range of tasks, ...
这两种自适应视角以部分参数共享的对抗式学习为指导,以利用它们的互惠性(mutual benefits)来减少端到端训练过程中的域转移。我们通过与各种最先进的方法进行比较,验证了方法在非配对MR到CT心脏分割中的适应性。实验结果表明,网络在Dice和ASD值方面都优于其他网络。我们的方法是通用的,可以很容易地扩展到其他无监督...
domain-invarianceoftheextractedfeaturesto-wardsthesegmentationtask.Thefeatureencoderlayersaresharedbybothperspectivestograsptheirmutualbenefitsdur-ingtheend-to-endlearningprocedure.Withoutusinganyan-notationfromthetargetdomain,thelearningofourunifiedmodelisguidedbyadversariallosses,withmultiplediscrim-inatorsemployed...
Balanced contrastive learning for long-tailed visual recognition. In: Proceedings of Conference on Computer Vision and Pattern Recognition, 2022. 6898–6907 Google Scholar Yang C, An Z, Zhou H, et al. Online knowledge distillation via mutual contrastive learning for visual recognition. IEEE Trans ...
The feature encoder is shared between both adaptive perspectives to leverage their mutual benefits via end-to-end learning. We have extensively evaluated our method with cardiac substructure segmentation and abdominal multi-organ segmentation for bidirectional cross-modality adaptation between MRI and CT ...