This paper presents a multimodal deep learning framework that utilizes
advanced image techniques to improve the performance of clinical analysis
heavily dependent on routinely acquired standard images. More specifically, we
develop a joint learning network that for the first time leverages the accuracy
and reproducibility of myocardial strains obtained from Displacement Encoding
with Stimulated Echo (DENSE) to guide the analysis of cine cardiac magnetic
resonance (CMR) imaging in late mechanical activation (LMA) detection. An image
registration network is utilized to acquire the knowledge of cardiac motions,
an important feature estimator of strain values, from standard cine CMRs. Our
framework consists of two major components: (i) a DENSE-supervised strain
network leveraging latent motion features learned from a registration network
to predict myocardial strains; and (ii) a LMA network taking advantage of the
predicted strain for effective LMA detection. Experimental results show that
our proposed work substantially improves the performance of strain analysis and
LMA detection from cine CMR images, aligning more closely with the achievements
of DENSE.

本研究提出了一种多模态深度学习框架，利用先进的图像技术来改善临床分析性能，该框架首次利用位移编码和激发回波（DENSE）获得的心肌应变的准确性和重复性，来引导晚期机械活化检测中电影心磁共振（CMR）成像的分析。实验证明，我们提出的方法显著改善了电影 CMR 图像的应变分析和晚期机械活化检测的性能，更接近于 DENSE 的成果。

多模态学习提升来自心脏 CineMR 图像的晚期机械激活检测

Multimodal Learning To Improve Cardiac Late Mechanical Activation  Detection From Cine MR Images

This paper presents our 3rd place solution in both Descriptor Track and
Matching Track of the Meta AI Video Similarity Challenge (VSC2022), a
competition aimed at detecting video copies. Our approach builds upon existing
image copy detection techniques and incorporates several strategies to exploit
on the properties of video data, resulting in a simple yet powerful solution.
By employing our proposed method, we achieved substantial improvements in
accuracy compared to the baseline results (Descriptor Track: 41% improvement,
Matching Track: 76% improvement). Our code is publicly available here:
this https URL

本论文介绍了我们在 Meta AI 视频相似性挑战赛（VSC2022）的 Descriptor Track 和 Matching Track 中的第三名解决方案，该竞赛旨在检测视频副本。我们的方法建立在现有的图像复制检测技术之上，并采用多种策略利用视频数据的特性，从而得到了一个简单而强大的解决方案。通过采用我们提出的方法，我们在准确性方面取得了相当大的改进，与基线结果相比（Descriptor Track：41％的改进，Matching Track：76％的改进）。