To deal with this problem, in this paper, we propose an object-guided instance segmentation method.
Cardiac tagging magnetic resonance imaging (t-MRI) is the gold standard for regional myocardium deformation and cardiac strain estimation.
User profiles can capture prominent user behaviors from the history, and provide valuable signals about which kinds of path patterns are more likely to lead to potential items of interest for the user.
We also analyze thickness patterns on different cardiac pathologies with a standard clinical model and the results demonstrate the potential clinical value of our method for thickness based cardiac disease diagnosis.
The accurate reconstruction of under-sampled magnetic resonance imaging (MRI) data using modern deep learning technology, requires significant effort to design the necessary complex neural network architectures.
The 3D volumetric shape of the heart's left ventricle (LV) myocardium (MYO) wall provides important information for diagnosis of cardiac disease and invasive procedure navigation.
To address this issue, in this work we extend the horizontal keypoint-based object detector to the oriented object detection task.
Ranked #10 on Oriented Object Detection on DOTA 1.0
Recent work on recommender systems has considered external knowledge graphs as valuable sources of information, not only to produce better recommendations but also to provide explanations of why the recommended items were chosen.
To alleviate such tedious and manual effort, in this paper we propose a novel weakly supervised segmentation framework based on partial points annotation, i. e., only a small portion of nuclei locations in each image are labeled.
There has been growing attention on fairness considerations recently, especially in the context of intelligent decision making systems.
The comparison results demonstrate the merits of our method in both Cobb angle measurement and landmark detection on low-contrast and ambiguous X-ray images.
In this paper, we propose a new box-based cell instance segmentation method.
The task of medical image segmentation commonly involves an image reconstruction step to convert acquired raw data to images before any analysis.
We consider an MRI reconstruction problem with input of k-space data at a very low undersampled rate.