Top-down instance segmentation framework has shown its superiority in object detection compared to the bottom-up framework.
To deal with this problem, in this paper, we propose an object-guided instance segmentation method.
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 #1 on Object Detection on DOTA
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.
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.
The spatial layout of the beams is regular, and this allows the beam features to be further fed into an efficient 2D convolutional neural network (CNN) for hierarchical feature aggregation.
Along with the instance normalization, the model is able to recover the target object distribution and suppress the distribution of neighboring attached objects.
In this paper, we propose a new box-based cell instance segmentation method.
We consider an MRI reconstruction problem with input of k-space data at a very low undersampled rate.