Abstract

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Title:: Detection of Meige’s syndrome based on multi-scale feature extraction and temporal segmentation

Author(s):: LI Bicao1; YI Benze1; WANG Bei2; LIU Zhitao3; GUO Xuwei4; WANG Yan1; 1. School of Information and Communication Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China; 2. UniversityInfirmary, Zhongyuan University of Technology, Zhengzhou 451191, China; 3. Diagnosis and Treatment Center of Meige’s Syndrome,the Third People’s Hospital of He’nan Province, Zhengzhou 450018, China; 4. Pediatric Department, the First Affiliated Hospital ofHe’nan University of Science and Technology, Luoyang 471000, China

Keywords:: Meige’s syndrome; temporal action detection; multi-scale feature extraction; temporal segmentation

Abstract:: Abstract: The diagnosis of Meige’s syndrome predominantly relies on the clinical assessment by physicians. Given thecomplexity and similarity of its symptoms to other neurological disorders, the diagnosis is crucial for both doctors andpatients. Herein a detection dataset for Meige’s syndrome is compiled from video recordings of 31 patients, and an automateddiagnostic system for Meige’s syndrome (MS-Net) applicable to untrimmed videos is developed. The system utilizesRetinaNet and UNet3+ to construct temporal detection and segmentation branches for multi-scale feature extraction andtemporal segmentation, obtains probability vectors for detection windows and the probability of disease onset per frame viathe decoding of temporal detection and segmentation branches, and finally generates a refined probability for each windowby processing the probability predictions from both branches using a multi-layer perceptron. The model performance isoptimized using additional loss functions and data augmentation techniques, operating on features interpretable by clinicalphysicians. MS-Net can assist in the diagnosis of Meige’s syndrome, improving the accuracy, convenience, and efficiency ofthe early diagnosis. The comparison of MS-Net with other state-of-the-art networks indicates that MS-Net achievescomparable performance in terms of average precision while utilizing interpretable features required in clinical practice.