Decoupling Disease, Covariates, and Individual Variability: A Unified Disentanglement Framework for Medical Image Classification

2026-09-22

AI Summary

本文提出MedIDL框架，通过将图像特征映射到三个正交潜在空间来解决医学图像分类中疾病相关特征与混淆变量和个人变异的分离问题。

Abstract

Accurately isolating disease-related features from confounding covariates (e.g., age, gender, site) and individual variations remains a fundamental challenge in medical image classification. Traditional regression-based approaches may ignore non-linear relations between image features and true covariates. To overcome this issue, we present a generalized Medical Imaging Disentanglement Learning (MedIDL) framework. MedIDL maps image features into three mutually orthogonal latent spaces through specialized disentanglement heads: a disease classification head guided by a supervised loss, a covariate-alignment head constrained by cross-subject similarity matching, and a Gaussian head absorbing individual variations. We evaluated our framework across 7 datasets encompassing diverse imaging modalities. MedIDL outperforms state-of-the-art supervised and self-supervised classification methods in accuracy across all datasets. Association analyses demonstrate that MedIDL successfully isolates target-specific latent representations. Gradient-based interpretability mappings localize pathognomonic patterns aligning with established clinical literature.

Problem

• disease-related features
• confounding covariates
• individual variations
• medical image classification

Innovation

• Medical Imaging Disentanglement Learning
• mutually orthogonal latent spaces
• covariate-alignment head
• Gaussian head
• supervised loss