# Regression Metric Loss: Learning a Semantic Representation Space for Medical Images

arXiv: https://arxiv.org/abs/2207.05231 · HTML: https://mlsanity.com/paper/2207.05231/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 12 Jul 2022.

## Builds on (most cited first, 9 of 9)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition](https://mlsanity.com/paper/1708.07632/) (2017)
- [Adaptive Contrast for Image Regression in Computer-Aided Disease Assessment](https://mlsanity.com/paper/2112.11700/) (2021)
- [AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability in Image Classification](https://mlsanity.com/paper/2004.09805/) (2020)
- [Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learning](https://mlsanity.com/paper/2111.07180/) (2021)
- [Semi-Supervised Learning for Bone Mineral Density Estimation in Hip X-ray Images](https://mlsanity.com/paper/2103.13482/) (2021)

## Built on by (most cited first, 1 of 1)

- [Prototype Learning for Explainable Brain Age Prediction](https://mlsanity.com/paper/2306.09858/) (2023)

## Abstract (arXiv)

Regression plays an essential role in many medical imaging applications for estimating various clinical risk or measurement scores. While training strategies and loss functions have been studied for the deep neural networks in medical image classification tasks, options for regression tasks are very limited. One of the key challenges is that the high-dimensional feature representation learned by existing popular loss functions like Mean Squared Error or L1 loss is hard to interpret. In this paper, we propose a novel Regression Metric Loss (RM-Loss), which endows the representation space with the semantic meaning of the label space by finding a representation manifold that is isometric to the label space. Experiments on two regression tasks, i.e. coronary artery calcium score estimation and bone age assessment, show that RM-Loss is superior to the existing popular regression losses on both performance and interpretability. Code is available at https://github.com/DIAL-RPI/Regression-Metric-Loss.