Decoupling of Multi-View Facial Features for Cushing’s Syndrome Diagnosis

Changwei Song¹, Jiaqi Qiang², Hongjun Liu¹, Jianqiang Li¹, Hui Pan², Qing Zhao^1,*, Jiuzuo Huang³, Shi Chen³
1 School of Computer Science, Beijing University of Technology, Beijing, China
2 Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
3 Department of Plastic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
* Corresponding Author: Qing Zhao. Email: email
(This article belongs to the Special Issue: Advances in Computational Intelligence for Complex Systems)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.083525

Received 05 April 2026; Accepted 15 June 2026; Published online 09 July 2026

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Abstract

Cushing’s syndrome (CS) is a rare endocrine disorder characterized by chronic hypercortisolism, and facial image-based intelligent diagnosis has emerged as a promising non-invasive approach. However, existing diagnostic models suffer from two core bottlenecks: inefficient fusion of deep semantic features and clinical prior features, and insufficient multi-view facial feature disentanglement without CS-specific pathophysiological constraints. To address these limitations, we propose a novel Multi-View Facial Feature Disentanglement Network (MVFFD-Net) for high-precision automatic CS diagnosis. The network takes five standard facial views (frontal, bilateral 45∘ oblique, and bilateral 90∘ lateral views) as input, with three key innovations: a bidirectional cross-attention module for synergistic fusion of deep and clinical prior features, a multi-path disentanglement architecture with multi-objective loss for separating view-invariant and view-specific features, and a graph attention fusion framework for adaptive multi-view feature integration. Unlike state-of-the-art models that mainly rely on single-view representations or generic multi-view fusion, MVFFD-Net explicitly integrates clinically guided multimodal fusion, CS-oriented feature disentanglement, and pathology-aware graph-based multi-view aggregation. Experimental results demonstrate that MVFFD-Net achieves an F1-score of 98.78% for CS diagnosis, significantly outperforming representative state-of-the-art methods in the current experimental setting. In this study, the task is defined as subject-level AI-assisted diagnosis to distinguish individuals with CS from matched controls using five standard facial views. Accordingly, the proposed framework is intended to provide non-invasive auxiliary diagnostic support rather than replace standard endocrinological evaluation and biochemical confirmation. More broadly, this clinically informed multi-view learning framework shows promising potential as a non-invasive auxiliary screening tool, though external multi-center validation remains necessary prior to large-scale clinical application.