What question did this study set out to answer?

This paper aims to enhance multi-disease diagnosis by integrating causal intervention into medical image analysis.

June 1, 2026

Beyond Correlation: Causal Intervention for Multi-Label Medical Image Diagnosis

Key Points

This paper aims to enhance multi-disease diagnosis by integrating causal intervention into medical image analysis.
Developed a framework for multi-label medical image diagnosis incorporating causal reasoning and backdoor adjustment.
Modelled latent disease-related confounders to refine predictions.
Conducted experiments on four medical imaging datasets: ODIR, LID-FFA, Endo, and Chestpert.
Proposed method outperformed existing approaches in diagnostic accuracy across all datasets.
Successfully identified true causal signals, reducing misattributions of features to diseases.
Model enhanced interpretability and reliability in diagnosing co-occurring diseases.

Abstract

This paper addresses the challenge of multi-disease diagnosis by integrating causal reasoning into the diagnostic framework. In clinical practice, multiple conditions often co-occur, making multi-disease diagnosis more relevant than isolated single-disease cases. However, most deep learning methods focus on single-disease detection and fail to capture the complexity of diagnosing concurrent conditions. Even in multi-label settings, existing approaches mainly rely on correlation-based inference, capturing statistical associations rather than true causal relationships. This can lead to spurious feature-disease associations, where features linked to one disease are mistakenly attributed to another due to frequent co-occurrence, ultimately undermines diagnostic accuracy and interpretability. To address this challenge, we propose a novel framework that incorporates causal intervention into multi-label medical image diagnosis, enabling the model to identify true causal signals rather than misleading correlations arising from co-occurring diseases. Specifically, we model latent disease-related confounders and apply backdoor adjustment to disentangle genuine causal effects from spurious associations. This is achieved by implicitly learning shared feature representations that serve as confounding variables, which are then used to refine image-derived features during prediction. The resulting causal adjustment allows the model to focus on disease-specific cues, improving accuracy and interpretability. Extensive experiments on four diverse medical imaging datasets: ODIR (color fundus photography), LID-FFA (fundus fluorescein angiography), Endo (colonoscopy), and Chestpert (X-ray) demonstrate that our method consistently outperforms existing approaches. Furthermore, our model also effectively separates the diagnosis of co-occurring diseases, high-lighting the potential of causal reasoning to enhance the reliability and clinical applicability of AI-assisted diagnosis. The source code is publicly available at https://github.com/davelailai/BankCausal.git.

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