Spatial pyramid based graph reasoning for semantic segmentation
The convolution operation suffers from a limited receptive filed, while global modeling is
fundamental to dense prediction tasks, such as semantic segmentation. In this paper, we …
fundamental to dense prediction tasks, such as semantic segmentation. In this paper, we …
Dense recurrent neural networks for accelerated MRI: History-cognizant unrolling of optimization algorithms
SAH Hosseini, B Yaman, S Moeller… - IEEE Journal of …, 2020 - ieeexplore.ieee.org
Inverse problems for accelerated MRI typically incorporate domain-specific knowledge
about the forward encoding operator in a regularized reconstruction framework. Recently …
about the forward encoding operator in a regularized reconstruction framework. Recently …
Dynamic graph convolutional autoencoder with node-attribute-wise attention for kidney and tumor segmentation from CT volumes
Extraction and integration of semantic connections, spatial relations and dependencies are
critical in volumetric image segmentation. This is a challenging issue, especially when there …
critical in volumetric image segmentation. This is a challenging issue, especially when there …
Multi-scale random walk driven adaptive graph neural network with dual-head neighboring node attention for CT segmentation
Segmenting objects with indistinct boundaries and large variations from CT volumes is a
challenging issue due to overlap** intensity distributions from neighboring tissues or long …
challenging issue due to overlap** intensity distributions from neighboring tissues or long …
[PDF][PDF] An Object Detection Framework Based on Deep Features and High-Quality Object Locations.
Accepted: 5 May 2021 Objection detection has long been a fundamental issue in computer
vision. Despite being widely studied, it remains a challenging task in the current body of …
vision. Despite being widely studied, it remains a challenging task in the current body of …
Graph based multi-scale neighboring topology deep learning for kidney and tumor segmentation
Objective. Effective learning and modelling of spatial and semantic relations between image
regions in various ranges are critical yet challenging in image segmentation tasks …
regions in various ranges are critical yet challenging in image segmentation tasks …
DrsNet: Dual-resolution semantic segmentation with rare class-oriented superpixel prior
Rare-class objects in natural scene images that are usually small and less frequent often
convey more important information for scene understanding than the common ones …
convey more important information for scene understanding than the common ones …
Global domain adaptation attention with data-dependent regulator for scene segmentation
Q Lei, F Lu - Plos one, 2024 - journals.plos.org
Most semantic segmentation works have obtained accurate segmentation results through
exploring the contextual dependencies. However, there are several major limitations that …
exploring the contextual dependencies. However, there are several major limitations that …
Win-win cooperation: Semantic encoding learning and saliency selection for weakly supervised semantic segmentation
Y Guo, X Liang, X Zheng, B Wu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Image-level weakly supervised semantic segmentation methods have attracted increasing
attention due to data labeling efficiency, but these methods mostly focus on utilizing the …
attention due to data labeling efficiency, but these methods mostly focus on utilizing the …
Two-Stage CNN Whole Heart Segmentation Combining Image Enhanced Attention Mechanism and Metric Classification
X Wang, F Wang, Y Niu - Journal of Digital Imaging, 2023 - Springer
Accurate segmentation of multiple tissues and organs in cardiac medical imaging is of great
value in computer-aided cardiovascular diagnosis. However, it is challenging due to the …
value in computer-aided cardiovascular diagnosis. However, it is challenging due to the …