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Weilin Fu
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2020 – today
- 2022
- [c9]Weilin Fu, Lennart Husvogt, Katharina Breininger
, Roman Schaffert, Omar Abu-Qamar, James G. Fujimoto, Andreas K. Maier:
Form Follows Function - Smart Network Design Enables Zero-shot Network Reuse. Bildverarbeitung für die Medizin 2022: 121-126 - 2021
- [j2]Weilin Fu
"Keep it simple, scholar": an experimental analysis of few-parameter segmentation networks for retinal vessels in fundus imaging. Int. J. Comput. Assist. Radiol. Surg. 16(6): 967-978 (2021) - [c8]Marcel Reimann
Novel Evaluation Metrics for Vascular Structure Segmentation. Bildverarbeitung für die Medizin 2021: 80-85 - [c7]Weilin Fu, Leonid Mill, Stephan Seitz, Tobias Geimer, Lasse Kling, Dennis Possart, Silke H. Christiansen, Andreas Maier:
Towards Mouse Bone X-ray Microscopy Scan Simulation. Bildverarbeitung für die Medizin 2021: 128-133 - 2020
- [c6]Weilin Fu, Katharina Breininger
Degenerating U-Net on Retinal Vessel Segmentation. Bildverarbeitung für die Medizin 2020: 33-38 - [c5]Weilin Fu, Lennart Husvogt, Stefan B. Ploner, James G. Fujimoto, Andreas K. Maier:
Modularization of Deep Networks Allows Cross-Modality Reuse. Bildverarbeitung für die Medizin 2020: 274-279 - [c4]Siming Bayer, Xia Zhong, Weilin Fu, Nishant Ravikumar
Imitation Learning Network for Fundus Image Registration Using a Divide-And-Conquer Approach. Bildverarbeitung für die Medizin 2020: 301-306 - [c3]Weilin Fu, Katharina Breininger
Abstract: Divide-And-Conquer Approach Towards Understanding Deep Networks. Bildverarbeitung für die Medizin 2020: 313
2010 – 2019
- 2019
- [j1]Andreas K. Maier
Learning with known operators reduces maximum error bounds. Nat. Mach. Intell. 1(8): 373-380 (2019) - [c2]Weilin Fu, Katharina Breininger
A Divide-and-Conquer Approach Towards Understanding Deep Networks. MICCAI (1) 2019: 183-191 - [i6]Andreas K. Maier, Christopher Syben, Bernhard Stimpel, Tobias Würfl, Mathis Hoffmann, Frank Schebesch, Weilin Fu, Leonid Mill, Lasse Kling, Silke H. Christiansen:
Learning with Known Operators reduces Maximum Training Error Bounds. CoRR abs/1907.01992 (2019) - [i5]Weilin Fu, Katharina Breininger, Roman Schaffert, Nishant Ravikumar, Andreas K. Maier:
A Divide-and-Conquer Approach towards Understanding Deep Networks. CoRR abs/1907.06194 (2019) - [i4]Weilin Fu, Lennart Husvogt, Stefan B. Ploner, James G. Fujimoto, Andreas K. Maier:
Lesson Learnt: Modularization of Deep Networks Allow Cross-Modality Reuse. CoRR abs/1911.02080 (2019) - [i3]Weilin Fu, Katharina Breininger, Zhaoya Pan, Andreas K. Maier:
What Do We Really Need? Degenerating U-Net on Retinal Vessel Segmentation. CoRR abs/1911.02660 (2019) - [i2]Siming Bayer, Xia Zhong, Weilin Fu, Nishant Ravikumar, Andreas Maier:
Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach. CoRR abs/1912.10837 (2019) - 2018
- [c1]Weilin Fu, Katharina Breininger, Roman Schaffert, Nishant Ravikumar
Frangi-Net. Bildverarbeitung für die Medizin 2018: 341-346 - 2017
- [i1]Weilin Fu, Katharina Breininger, Tobias Würfl, Nishant Ravikumar, Roman Schaffert, Andreas K. Maier:
Frangi-Net: A Neural Network Approach to Vessel Segmentation. CoRR abs/1711.03345 (2017)
Coauthor Index
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