Work

Ordinal Regression Research Based on Dual Loss Function—An Example on Lumbar Vertebra Classification in CT Images

Year: 2025

Type: article

Abstract: Background/Objectives: Some classification problems involve ordered categories (e.g., low–medium–high), which are better modeled as ordinal regression. This study aimed to propose and evaluate a dual loss framework—Ordinal Residual Dual Loss—for lumbar vertebra classification on CT images to assist L3 identification and sarcopenia detection. Methods: In this retrospective study, lumbar spine CT images were used to train a deep learning model based on a MobileNet-v3-Large network. The proposed framework combines standard cross-entropy loss for classification with an Ordinal Residual Loss defined on the difference between output probabilities and target ordinal probabilities. Results: Experimental results show that the Ordinal Residual Dual Loss approach outperforms using cross-entropy alone and also surpasses methods from previous studies in lumbar vertebra classification performance. Conclusions: Leveraging a dual loss design that incorporates ordinal information improves vertebral level classification on CT images and has potential to support more accurate automated L3 localization and sarcopenia assessment in clinical practice.

Source: Diagnostics

DOI: 10.3390/diagnostics15232949

Authors: Chia-Pei Tang, Hong-Yi Chang, Yu-Ming Hsu, Tu-Liang Lin

Institutions: Tzu Chi University, Buddhist Tzu Chi Medical Foundation, Dalin Tzu Chi Hospital, National Chiayi University

Language: English

Topic: Medical Imaging and Analysis

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,137