19 citations · 34 across the 9 of their papers we have counts for
9 papers
Hardware-aware Real-time Myocardial Segmentation Quality Control in Contrast Echocardiography
Dewen Zeng, Yukun Ding, Haiyun Yuan +5
Automatic myocardial segmentation of contrast echocardiography has shown great potential in the quantification of myocardial perfusion parameters. Segmentation quality control is a…
Segmentation with Multiple Acceptable Annotations: A Case Study of Myocardial Segmentation in Contrast Echocardiography
Dewen Zeng, Mingqi Li, Yukun Ding +7
Most existing deep learning-based frameworks for image segmentation assume that a unique ground truth is known and can be used for performance evaluation. This is true for many app…
Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images
Xiaowe Xu, Jiawei Zhang, Jinglan Liu +10
As one of the most commonly ordered imaging tests, computed tomography (CT) scan comes with inevitable radiation exposure that increases the cancer risk to patients. However, CT im…
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling
Akash Srivastava, Yamini Bansal, Yukun Ding +15
Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the (aggregate) posterior to encourage statistical independence of the…
Multi-Cycle-Consistent Adversarial Networks for CT Image Denoising
Jinglan Liu, Yukun Ding, Jinjun Xiong +6
CT image denoising can be treated as an image-to-image translation task where the goal is to learn the transform between a source domain (noisy images) and a target domain …
Real-Time Boiler Control Optimization with Machine Learning
Yukun Ding, Yiyu Shi
In coal-fired power plants, it is critical to improve the operational efficiency of boilers for sustainability. In this work, we formulate real-time boiler control as an optimizati…
Revisiting the Evaluation of Uncertainty Estimation and Its Application to Explore Model Complexity-Uncertainty Trade-Off
Yukun Ding, Jinglan Liu, Jinjun Xiong +1
Accurately estimating uncertainties in neural network predictions is of great importance in building trusted DNNs-based models, and there is an increasing interest in providing acc…
PBGen: Partial Binarization of Deconvolution-Based Generators for Edge Intelligence
Jinglan Liu, Jiaxin Zhang, Yukun Ding +3
This work explores the binarization of the deconvolution-based generator in a GAN for memory saving and speedup of image construction. Our study suggests that different from convol…
On the Universal Approximability and Complexity Bounds of Quantized ReLU Neural Networks
Yukun Ding, Jinglan Liu, Jinjun Xiong +1
Compression is a key step to deploy large neural networks on resource-constrained platforms. As a popular compression technique, quantization constrains the number of distinct weig…