Skip to search formSkip to main contentSkip to account menu
Optimal Surface Segmentation in Volumetric Images-A Graph-Theoretic Approach
TLDR
An optimal surface detection method capable of simultaneously detecting multiple interacting surfaces, in which the optimality is controlled by the cost functions designed for individual surfaces and by several geometric constraints defining the surface smoothness and interrelations is developed.
View on IEEE
Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
TLDR
A deep active learning framework that combines fully convolutional network (FCN) and active learning to significantly reduce annotation effort by making judicious suggestions on the most effective annotation areas is presented.
View PDF on arXiv
Task scheduling and voltage selection for energy minimization
TLDR
A two-phase framework that integrates task assignment, ordering and voltage selection together to minimize energy consumption of real-time dependent tasks executing on a given number of variable voltage processors is presented.
View on ACM
Optimal Net Surface Problems with Applications
TLDR
It is proved that the optimal net surface problems on general d-D multicolumn graphs (d ? 3) are NP-hard, and two combinatorial optimization problems called optimal netsurface problems on such graphs are formulated.
View via Publisher
Deep Adversarial Networks for Biomedical Image Segmentation Utilizing Unannotated Images
TLDR
A new deep adversarial network (DAN) model is proposed for biomedical image segmentation, aiming to attain consistently good segmentation results on both annotated and unannotated images.
View via Publisher
Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation
TLDR
This paper proposes a new DL framework for 3D image segmentation, based on a fully convolutional network (FCN) and a recurrent neural network (RNN), which are responsible for exploiting the intra-slice and inter-slice contexts, respectively.
View PDF on arXiv
A Hierarchical Graph Network for 3D Object Detection on Point Clouds
TLDR
A new graph convolution (GConv) based hierarchical graph network (HGNet) for 3D object detection, which processes raw point clouds directly to predict 3D bounding boxes and outperforms state-of-the-art methods on two large-scale point cloud datasets.
View on IEEE
Algorithms on Minimizing the Maximum Sensor Movement for Barrier Coverage of a Linear Domain
TLDR
The problem of moving sensors on a line to form a barrier coverage of a specified segment of the line such that the maximum moving distance of the sensors is minimized is minimized by giving an O(n^2\log n)$$O(n2logn) time algorithm.
View on Springer
A Framework for Identifying Diabetic Retinopathy Based on Anti-noise Detection and Attention-Based Fusion
TLDR
A new framework for unifying lesion detection and DR identification is proposed, which outperforms state-of-the-art methods on two grand challenge retina datasets, EyePACS and Messidor.
View via Publisher
An optimal algorithm for shortest paths on weighted interval and circular-arc graphs, with applications
We give the first linear-time algorithm for computing single-source shortest paths in a weighted interval or circular-arc graph, when we are given the model of that graph, i.e., the actual weighted…
View on Springer
...
...
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy, Terms of Service, and Dataset License