A Parts Based Registration Loss for Detecting Knee Joint Areas
Abstract: In this paper, a parts based loss is considered for finetune registering knee joint areas. Here the parts are defined as abstract feature vectors with location and they are automatically selected from a reference image. For a test image the detected parts are encouraged to have a similar spatial configuration than the corresponding parts in the reference image.
- R. Girshick. Fast R-CNN. In 2015 IEEE International Conference on Computer Vision (ICCV), pages 1440–1448, 2015.
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. In Advances in Neural Information Processing Systems, pages 91–99, 2015.
- You Only Look Once: Unified, Real-Time Object Detection. In CVPR 2016, pages 779–788, 2016.
- J. Tiirola. A Neural Template Matching Method to Detect Knee Joint Areas. In arXiv, 2209.11791, 2022.
- Combining Compositional Models and Deep Networks For Robust Object Classification under Occlusion. In IEEE Winter Conference on Applications of Computer Vision, WACV 2020, pages 1322–1330, 2020.
- Integrating spatial configuration into heatmap regression based CNNs for landmark localization. In Medical Image Analysis, volume 54, pages 207–219, 2019.
- Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation. In Advances in Neural Information Processing Systems, volume 27, pages 1799–1807, 2014.
- This Looks Like That: Deep Learning for Interpretable Image Recognition. In Advances in Neural Information Processing Systems, volume 32, pages 8930–8941, 2019.
- K. Simonyan and A. Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition. In 3rd International Conference on Learning Representations, 2015.
- Spatial transformer networks. In Advances in Neural Information Processing Systems, volume 28, 2015.
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