Deep Diffeomorphic Transformer Networks
Spatial Transformer layers allow neural networks, at least in principle, to be invariant to large spatial transformations in image data. The model has, however, seen limited uptake as most practical implementations support only transformations that are too restricted, e.g. affine or homographic maps, and/or destructive maps, such as thin plate splines. We investigate the use of ï¬exible diffeomorphic image transformations within such networks and demonstrate that significant performance gains can be attained over currently-used models. The learned transformations are found to be both simple and intuitive, thereby providing insights into individual problem domains. With the proposed framework, a standard convolutional neural network matches state-of-the-art results on face veriï¬cation with only two extra lines of simple TensorFlow code.
Code (1)
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Diffeomorphic Spatial Transformer Networks
In this paper we propose a spatial transformer network where the spatial transformations are limited to the group of diffeomorphisms. Diffeomorphic transformations are a kind of homeomorphism, which by definition preserv…
Residual Networks as Flows of Velocity Fields for Diffeomorphic Time Series Alignment
Non-linear (large) time warping is a challenging source of nuisance in time-series analysis. In this paper, we propose a novel diffeomorphic temporal transformer network for both pairwise and joint time-series alignment.…
Time SeriesTime Series AlignmentTime Series AnalysisDiffeomorphic Transformer-based Abdomen MRI-CT Deformable Image Registration
This paper aims to create a deep learning framework that can estimate the deformation vector field (DVF) for directly registering abdominal MRI-CT images. The proposed method assumed a diffeomorphic deformation. By using…
Image RegistrationTransMorph: Transformer for unsupervised medical image registration
In the last decade, convolutional neural networks (ConvNets) have been a major focus of research in medical image analysis. However, the performances of ConvNets may be limited by a lack of explicit consideration of the …
Image RegistrationMedical Image AnalysisMedical Image RegistrationDiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations
Pre-trained Vision Transformers now serve as powerful tools for computer vision. Yet, efficiently adapting them for multiple tasks remains a challenge that arises from the need to modify the rich hidden representations e…
Multi-Task Learning