147 citations · 213 across the 8 of their papers we have counts for
6 papers · 1 filter
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…