We present a pipeline for unbiased and robust multimodal registration of
neuroimaging modalities with minimal pre-processing. While typical multimodal
studies need to use multiple independent processing pipelines, with diverse
options and hyperparameters, we propose a single and structured framework to
jointly process different image modalities. The use of state-of-the-art
learning-based techniques enables fast inferences, which makes the presented
method suitable for large-scale and/or multi-cohort datasets with a diverse
number of modalities per session. The pipeline currently works with structural
MRI, resting state fMRI and amyloid PET images. We show the predictive power of
the derived biomarkers using in a case-control study and study the cross-modal
relationship between different image modalities. The code can be found in
https: //github.com/acasamitjana/JUMP.

利用最少的预处理提供无偏差和稳健的多模式神经影像注册，通过单一结构化框架联合处理不同图像模式，使用先进的基于学习的技术实现快速推论，适用于大规模和 / 或多队列数据集，目前可处理结构 MRI、静息状态 fMRI 和淀粉样 PET 图像，通过案例对照研究证明了所得生物标志物的预测能力，并研究了不同图像模式之间的跨模式关系。

JUMP: 一种用于神经影像的联合多模态注册流程的最小预处理方法

JUMP: A joint multimodal registration pipeline for neuroimaging with  minimal preprocessing

Active speaker detection and speech enhancement have become two increasingly
attractive topics in audio-visual scenario understanding. According to their
respective characteristics, the scheme of independently designed architecture
has been widely used in correspondence to each single task. This may lead to
the representation learned by the model being task-specific, and inevitably
result in the lack of generalization ability of the feature based on
multi-modal modeling. More recent studies have shown that establishing
cross-modal relationship between auditory and visual stream is a promising
solution for the challenge of audio-visual multi-task learning. Therefore, as a
motivation to bridge the multi-modal associations in audio-visual tasks, a
unified framework is proposed to achieve target speaker detection and speech
enhancement with joint learning of audio-visual modeling in this study.

提出了一个统一框架，通过视听联合建模来实现目标说话人检测和语音增强，以建立音频 - 视觉任务中的多模态关联。