147 citations · 213 across the 8 of their papers we have counts for
8 papers
Time Series Analysis in American Stock Market Recovering in Post COVID-19 Pandemic Period
Weilin Fu, Zhuoran Li, Yupeng Zhang +1
Every financial crisis has caused a dual shock to the global economy. The shortage of market liquidity, such as default in debt and bonds, has led to the spread of bankruptcies, su…
Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach
Siming Bayer, Xia Zhong, Weilin Fu +2
Comparison of microvascular circulation on fundoscopic images is a non-invasive clinical indication for the diagnosis and monitoring of diseases, such as diabetes and hypertensions…
What Do We Really Need? Degenerating U-Net on Retinal Vessel Segmentation
Weilin Fu, Katharina Breininger, Zhaoya Pan +1
Retinal vessel segmentation is an essential step for fundus image analysis. With the recent advances of deep learning technologies, many convolutional neural networks have been app…
Lesson Learnt: Modularization of Deep Networks Allow Cross-Modality Reuse
Weilin Fu, Lennart Husvogt, Stefan Ploner James G. Fujimoto Andreas Maier
Fundus photography and Optical Coherence Tomography Angiography (OCT-A) are two commonly used modalities in ophthalmic imaging. With the development of deep learning algorithms, fu…
Bayesian Symbolic Regression
Ying Jin, Weilin Fu, Jian Kang +2
Interpretability is crucial for machine learning in many scenarios such as quantitative finance, banking, healthcare, etc. Symbolic regression (SR) is a classic interpretable machi…
A Divide-and-Conquer Approach towards Understanding Deep Networks
Weilin Fu, Katharina Breininger, Roman Schaffert +2
Deep neural networks have achieved tremendous success in various fields including medical image segmentation. However, they have long been criticized for being a black-box, in that…
Learning with Known Operators reduces Maximum Training Error Bounds
Andreas K. Maier, Christopher Syben, Bernhard Stimpel +7
We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain op…
Frangi-Net: A Neural Network Approach to Vessel Segmentation
Weilin Fu, Katharina Breininger, Tobias Würfl +3
In this paper, we reformulate the conventional 2-D Frangi vesselness measure into a pre-weighted neural network ("Frangi-Net"), and illustrate that the Frangi-Net is equivalent to…