HaipengXiong / weighted-hausdorff-lossLinks
A loss function (Weighted Hausdorff Distance) for object localization in PyTorch
☆91Updated 6 years ago
Alternatives and similar repositories for weighted-hausdorff-loss
Users that are interested in weighted-hausdorff-loss are comparing it to the libraries listed below
Sorting:
- Official PyTorch implementation of "End to End Trainable Active Contours via Differentiable Rendering"☆92Updated 5 years ago
- Official PyTorch implementation of "Unsupervised Microvascular Image Segmentation Using an Active Contours Mimicking Neural Network"☆73Updated 4 years ago
- ☆57Updated 5 years ago
- matlab and python wrap of crf and dense crf, both 2d and 3d are supported☆169Updated 2 years ago
- ☆95Updated 6 years ago
- Code for our arxiv preprint: https://arxiv.org/abs/1904.05236☆34Updated 6 years ago
- 3D volume data augmentation package inspired by albumentations☆79Updated 5 years ago
- SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth☆69Updated 6 years ago
- Implementation of Hausdorff loss function for DNN learning in segmentation tasks.☆66Updated last year
- Implementation of active contour loss function☆201Updated 5 years ago
- Non-local U-Nets for Biomedical Image Segmentation☆105Updated 5 years ago
- Repository for the article "Unsupervised domain adaptation for medical imaging segmentation with self-ensembling".☆43Updated 6 years ago
- pyTorch implementation of clDice☆29Updated 5 years ago
- PyTorch implementation of Foveation for Segmentation of Ultra-High Resolution Images☆41Updated 3 years ago
- An unofficial pytorch implementation for "Learning Active Contour Models for Medical Image Segmentation" by Chen, Xu, et al.☆75Updated 6 months ago
- [Medical Image Analysis 2019] Attentive Neural Cell Instance Segmentation☆47Updated 3 years ago
- Oral presentation at MIDL 2020 - Bounding boxes for weakly supervised segmentation: Global constraints get close to full supervision☆47Updated 4 years ago
- Probabilistic Dense Displacement Network (3D discrete deep learning registration)