BMC oral health · 2026
A self-supervised pre-training method for lesion segmentation in oral potentially malignant disorders.
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Cao Yuqi, Lu Miao, Zhang Jiayuan, Zhang Rui, Shi Linjun, Dan Hongxia, Huang Pingjie, Zhang Guangxin, Chen Qianming
Аннотация
OBJECTIVE: Supervised training for oral potentially malignant disorder (OPMD) image segmentation requires expensive annotated data, particularly scarce in remote regions with few medical specialists. This study aims to propose a self-guided pre-training method to automatically extract features from unlabeled images, enhancing the performance of OPMD lesion segmentation.
MATERIALS AND METHODS: This study utilized 3,417 OPMD photographs from ZJUSS as the internal dataset and two independent external datasets from WCHS and CS-SJTU for validation. The internal labeled dataset was evaluated using five independent patient-level train/validation/test splits to prevent patient-level data leakage. We proposed a multiscale separable attention masked image model, MS-SAMIM, and a self-guided mask generation method. To enhance robustness, we incorporated a teacher-student consistency constraint and a contrastive learning constraint.
RESULTS: Our model achieved 84.35% Dice, 74.65% IoU, 81.84% sensitivity, and 88.02% precision in OPMD lesion segmentation, surpassing various advanced fully supervised and other self-supervised approaches. In the external validation, the model achieved 82.15% Dice, 70.09% IoU, 77.96% sensitivity, and 87.84% precision on external dataset #1 and 81.18% Dice, 69.71% IoU, 78.58% sensitivity, and 84.39% precision on external dataset #2.
CONCLUSIONS: The proposed self-guided pre-training strategy may reduce reliance on pixel-level annotations and improve OPMD lesion segmentation from clinical photographs. Further prospective validation is required before clinical deployment.
MATERIALS AND METHODS: This study utilized 3,417 OPMD photographs from ZJUSS as the internal dataset and two independent external datasets from WCHS and CS-SJTU for validation. The internal labeled dataset was evaluated using five independent patient-level train/validation/test splits to prevent patient-level data leakage. We proposed a multiscale separable attention masked image model, MS-SAMIM, and a self-guided mask generation method. To enhance robustness, we incorporated a teacher-student consistency constraint and a contrastive learning constraint.
RESULTS: Our model achieved 84.35% Dice, 74.65% IoU, 81.84% sensitivity, and 88.02% precision in OPMD lesion segmentation, surpassing various advanced fully supervised and other self-supervised approaches. In the external validation, the model achieved 82.15% Dice, 70.09% IoU, 77.96% sensitivity, and 87.84% precision on external dataset #1 and 81.18% Dice, 69.71% IoU, 78.58% sensitivity, and 84.39% precision on external dataset #2.
CONCLUSIONS: The proposed self-guided pre-training strategy may reduce reliance on pixel-level annotations and improve OPMD lesion segmentation from clinical photographs. Further prospective validation is required before clinical deployment.