# A Parts Based Registration Loss for Detecting Knee Joint Areas

arXiv: https://arxiv.org/abs/2307.00083 · HTML: https://mlsanity.com/paper/2307.00083/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 10 references that are in the map.
- First posted on arXiv 30 Jun 2023.

## Builds on (most cited first, 10 of 10)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [This Looks Like That: Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/1806.10574/) (2018)
- [Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation](https://mlsanity.com/paper/1406.2984/) (2014)
- [Integrating Spatial Configuration into Heatmap Regression Based CNNs for Landmark Localization](https://mlsanity.com/paper/1908.00748/) (2019)
- [Combining Compositional Models and Deep Networks For Robust Object Classification under Occlusion](https://mlsanity.com/paper/1905.11826/) (2019)
- [A Neural Template Matching Method to Detect Knee Joint Areas](https://mlsanity.com/paper/2209.11791/) (2022)

## Abstract (arXiv)

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.