@Article{cmes.2026.083525,
AUTHOR = {Changwei Song, Jiaqi Qiang, Hongjun Liu, Jianqiang Li, Hui Pan, Qing Zhao, Jiuzuo Huang, Shi Chen},
TITLE = {Decoupling of Multi-View Facial Features for Cushing’s Syndrome Diagnosis},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/CMES/online/detail/27507},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2026.083525}
}