RIVER-NET: A Multi-Branch Progressive Aggregation Network for Precise Segmentation of Oral Mucosal Lesions

Accurate segmentation of oral mucosal lesions remains challenging because lesion regions often resemble surrounding mucosa, with subtle color differences, weak texture contrast, unclear boundaries, and distractors such as saliva, folds, reflections, and uneven illumination. This study proposes RIVER-NET to improve oral mucosal lesion delineation in complex intraoral images. RIVER-NET is a U-shaped multi-branch progressive aggregation network integrating three task-oriented modules. The Multi-Scale Residual Unit (MSRU) captures multi-scale mucosal texture changes and irregular lesion morphology before spatial downsampling. Dynamic Gated Context Fusion (DGCF) enables gated encoder–decoder context interaction to suppress lesion-like distractors while preserving lesion details. Multi-scale Alignment and Attentive Fusion (MAAF) aligns and attentively aggregates multi-level features to reduce semantic–spatial misalignment and enhance boundary recovery. On the Oral Pathological Mucosa (OPM) dataset, RIVER-NET achieved a Dice score of 75.59% and an IoU of 0.632, outperforming U-Net by 30.88 percentage points in Dice and nnU-Net by 5.23 percentage points. Component-wise and parameter-matched replacement results were consistent with complementary effects of MSRU, DGCF, and MAAF under the current experimental setting. On the independent public ISIC dataset, on which models were trained and evaluated under five-fold cross-validation, RIVER-NET achieved 94.62 ± 2.18% Dice and 0.900 ± 0.039 IoU. Without fine-tuning on the external Autooral dataset, it achieved the highest Dice (0.5547) and specificity (0.9324). RIVER-NET showed promising segmentation performance on the evaluated datasets. These findings demonstrate algorithm-level feasibility but do not establish readiness for clinical use.