Embedding Tasks Into the Latent Space: Cross-Space Consistency for Multi-Dimensional Analysis in Echocardiography.

Multi-dimensional analysis in echocardiography has attracted attention due to its potential for clinical indices quantification and computer-aided diagnosis. It can utilize various information to provide the estimation of multiple cardiac indices. However, it still has the challenge of inter-task conflict.

Distilling sub-space structure across views for cardiac indices estimation.

Cardiac indices estimation in multi-view images attracts great attention due to its capability for cardiac function assessment. However, the variation of the cardiac indices across views causes that most cardiac indices estimation methods can only be trained separately in each view, resulting in low data utilization. To solve this problem, we have proposed distilling the sub-space structure across views to explore the multi-view data fully for cardiac indices estimation.

Phylogenetic Analyses of and New Species from China.

is an ectomycorrhizal fungal genus with hypogeous ascomata in the family (). Molecular analyses of using both single (ITS) and concatenated gene datasets (ITS-nLSU) showed a total of 223 sequences, including 92 newly gained sequences from Chinese specimens. Phylogenetic results based on these two datasets revealed seven distinct phylogenetic clades.

Multitask Learning for Estimating Multitype Cardiac Indices in MRI and CT Based on Adversarial Reverse Mapping.

The estimation of multitype cardiac indices from cardiac magnetic resonance imaging (MRI) and computed tomography (CT) images attracts great attention because of its clinical potential for comprehensive function assessment. However, the most exiting model can only work in one imaging modality (MRI or CT) without transferable capability. In this article, we propose the multitask learning method with the reverse inferring for estimating multitype cardiac indices in MRI and CT.

Pyramid feature adaptation for semi-supervised cardiac bi-ventricle segmentation.

Cardiac bi-ventricle segmentation (BVS) is an essential task for assessing cardiac indices, such as the ejection fraction and volume of the left ventricle (LV) and right ventricle (RV). However, BVS is extremely challenging due to the high variability of the bi-ventricle structure and lack of labeled data. In this paper, we propose a pyramid feature adaptation based semi-supervised method (PABVS) for cardiac bi-ventricle segmentation.