DeepAI AI Chat
Log In Sign Up

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

by   Hanqing Chao, et al.
Rensselaer Polytechnic Institute

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


page 7

page 12


Segmentation Loss Odyssey

Loss functions are one of the crucial ingredients in deep learning-based...

Adaptive Contrast for Image Regression in Computer-Aided Disease Assessment

Image regression tasks for medical applications, such as bone mineral de...

Revisiting the Loss Weight Adjustment in Object Detection

By definition, object detection requires a multi-task loss in order to s...

ComboLoss for Facial Attractiveness Analysis with Squeeze-and-Excitation Networks

Loss function is crucial for model training and feature representation l...

Label Encoding for Regression Networks

Deep neural networks are used for a wide range of regression problems. H...

Learning Label Encodings for Deep Regression

Deep regression networks are widely used to tackle the problem of predic...

Medical Image Retrieval via Nearest Neighbor Search on Pre-trained Image Features

Nearest neighbor search (NNS) aims to locate the points in high-dimensio...