# Symmetric Perception and Ordinal Regression for Detecting Scoliosis Natural Image

arXiv: https://arxiv.org/abs/2411.15799 · HTML: https://mlsanity.com/paper/2411.15799/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 62 references that are in the map.
- First posted on arXiv 24 Nov 2024.

## Builds on (most cited first, 27 of 27)

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- [Deep Ordinal Regression Network for Monocular Depth Estimation](https://mlsanity.com/paper/1806.02446/) (2018)
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- [Beyond Planar Symmetry: Modeling human perception of reflection and rotation symmetries in the wild](https://mlsanity.com/paper/1704.03568/) (2017)
- [Reflection and Rotation Symmetry Detection via Equivariant Learning](https://mlsanity.com/paper/2203.16787/) (2022)
- [Learning to Discover Reflection Symmetry via Polar Matching Convolution](https://mlsanity.com/paper/2108.12952/) (2021)
- [Seg4Reg+: Consistency Learning between Spine Segmentation and Cobb Angle Regression](https://mlsanity.com/paper/2208.12462/) (2022)
- [Learning from Label Relationships in Human Affect](https://mlsanity.com/paper/2207.05577/) (2022)
- [Facial Action Unit Detection via Adaptive Attention and Relation](https://mlsanity.com/paper/2001.01168/) (2020)
- [Facial Action Unit Detection by Adaptively Constraining Self-Attention and Causally Deconfounding Sample](https://mlsanity.com/paper/2410.01251/) (2024)

## Abstract (arXiv)

Scoliosis is one of the most common diseases in adolescents. Traditional screening methods for the scoliosis usually use radiographic examination, which requires certified experts with medical instruments and brings the radiation risk. Considering such requirement and inconvenience, we propose to use natural images of the human back for wide-range scoliosis screening, which is a challenging problem. In this paper, we notice that the human back has a certain degree of symmetry, and asymmetrical human backs are usually caused by spinal lesions. Besides, scoliosis severity levels have ordinal relationships. Taking inspiration from this, we propose a dual-path scoliosis detection network with two main modules: symmetric feature matching module (SFMM) and ordinal regression head (ORH). Specifically, we first adopt a backbone to extract features from both the input image and its horizontally flipped image. Then, we feed the two extracted features into the SFMM to capture symmetric relationships. Finally, we use the ORH to transform the ordinal regression problem into a series of binary classification sub-problems. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods as well as human performance, which provides a promising and economic solution to wide-range scoliosis screening. In particular, our method achieves accuracies of 95.11% and 81.46% in estimation of general severity level and fine-grained severity level of the scoliosis, respectively.