@@ -53,14 +53,21 @@ Predictive models are an integral part of medical image analysis, forming the ba
For example see:
*[Lin et al., DTUNet: Learning topological similarity for curvilinear structure segmentation, Information Processing in Medical Imaging (IPMI) 2023](https://arxiv.org/abs/2205.11115)
*[Zepf et al: That Label's Got Style, International Conference on Learning Representations (ICLR) 2023](https://openreview.net/forum?id=wZ2SVhOTzBX)
*[Czolbe, ..., and Feragen: Is segmentation uncertainty useful?, Information Processing in Medical Imaging (IPMI) 2021](https://link.springer.com/chapter/10.1007/978-3-030-78191-0_55)
*[Czolbe, Feragen and Krause: Spot the Difference: Detection of Topological Changes
via Geometric Alignment, Neural Information Processing Systems (NeurIPS) 2021](https://proceedings.neurips.cc/paper/2021/file/7867d6557b82ed3b5d61e6591a2a2fd3-Paper.pdf)
*[Petersen et al: Feature robustness and sex differences in medical imaging: a case study in MRI-based Alzheimer's disease detection, Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwiY1c6frb38AhVqRvEDHaDDA9cQFnoECB4QAQ&url=https%3A%2F%2Fwww.springerprofessional.de%2Ffeature-robustness-and-sex-differences-in-medical-imaging-a-case%2F23500000&usg=AOvVaw33I4dX-c9GMft0uMATSNuy)
*[Lin et al: I saw, I conceived, I concluded: Progressive Concepts as Bottlenecks, arXiv 2022](https://arxiv.org/abs/2211.10630)
## Topology aware learning for medical imaging
While most modern medical imaging takes a very local, pixel-focused approach to problems such as image segmentation or registration, these often lead to suboptimal performance when viewed globally, in the sense that topological constraints naturally inherent in the data are violated. This can take the form of segmented structures taking on an incorrect topology, or image registration algorithms incorrectly representing the topology of the underlying anatomy. Our research includes topology-aware deep learning models for medical image processing.
For more information, see:
*[Lin et al., DTUNet: Learning topological similarity for curvilinear structure segmentation, Information Processing in Medical Imaging (IPMI) 2023](https://arxiv.org/abs/2205.11115)
*[Czolbe, Feragen and Krause: Spot the Difference: Detection of Topological Changes
via Geometric Alignment, Neural Information Processing Systems (NeurIPS) 2021](https://proceedings.neurips.cc/paper/2021/file/7867d6557b82ed3b5d61e6591a2a2fd3-Paper.pdf)
*[Kok-Nielsen, ..., Feragen: Topaware: Topology-aware registration, Medical Image Computing and Computer Assisted Intervention (MICCAI) 2019](https://link.springer.com/chapter/10.1007/978-3-030-32245-8_41)
