4 citations · 8 across the 5 of their papers we have counts for
6 papers
A Unified Variational Framework for Deep Weakly Supervised Image Segmentation
Yin King Chu, Lingfeng Li, Sung Ha Kang +2
We propose a unified variational framework for image segmentation under sparse pixel-level supervision. Our method is based on a simplex-constrained Potts model with a smooth perim…
Solving Convolution-type Integral Equations using Preconditioned Neural Operators
Raymond Chan, Lingfeng Li
Convolution-type integral equations arise from various fields, \textit{e.g.}, finite impulse response filters in signal processing and deblurring problems in image processing. When…
Deep Convolutional Neural Networks Meet Variational Shape Compactness Priors for Image Segmentation
Kehui Zhang, Lingfeng Li, Hao Liu +2
Shape compactness is a key geometrical property to describe interesting regions in many image segmentation tasks. In this paper, we propose two novel algorithms to solve the introd…
BP-DeepONet: A new method for cuffless blood pressure estimation using the physcis-informed DeepONet
Lingfeng Li, Xue-Cheng Tai, Raymond Chan
Cardiovascular diseases (CVDs) are the leading cause of death worldwide, with blood pressure serving as a crucial indicator. Arterial blood pressure (ABP) waveforms provide continu…
Priori Error Estimate of Deep Mixed Residual Method for Elliptic PDEs
Lingfeng Li, Xue-cheng Tai, Jiang Yang +1
In this work, we derive a priori error estimate of the mixed residual method when solving some elliptic PDEs. Our work is the first theoretical study of this method. We prove that…
Generalization Error Analysis of Neural networks with Gradient Based Regularization
Lingfeng Li, Xue-Cheng Tai, Jiang Yang
We study gradient-based regularization methods for neural networks. We mainly focus on two regularization methods: the total variation and the Tikhonov regularization. Applying the…