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Optimal Surface Segmentation in Volumetric Images-A Graph-Theoretic Approach
- Kang Li, Xiaodong Wu, D. Chen, M. Sonka
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine…
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.
Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
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.
Task scheduling and voltage selection for energy minimization
- Yumin Zhang, X. Hu, D. Chen
- Computer ScienceProceedings Design Automation Conference (IEEE…
- 10 June 2002
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.
Optimal Net Surface Problems with Applications
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.
Deep Adversarial Networks for Biomedical Image Segmentation Utilizing Unannotated Images
- Yizhe Zhang, L. Yang, Jianxu Chen, Maridel Fredericksen, David P. Hughes, D. Chen
- Computer ScienceMICCAI
- 10 September 2017
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.
Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation
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.
A Hierarchical Graph Network for 3D Object Detection on Point Clouds
- Jintai Chen, Biwen Lei, Qingyu Song, Haochao Ying, D. Chen, Jian Wu
- Computer ScienceIEEE/CVF Conference on Computer Vision and…
- 1 June 2020
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.
Algorithms on Minimizing the Maximum Sensor Movement for Barrier Coverage of a Linear Domain
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.
A Framework for Identifying Diabetic Retinopathy Based on Anti-noise Detection and Attention-Based Fusion
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.
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…