SparseSegNet: A Boundary-Aware Lightweight Segmentation Architecture for Skin Lesions

Accurate skin lesion segmentation is essential for the early diagnosis of dermatological conditions, including the timely detection of malignant skin cancers. Enabling such analysis on personal devices—such as smartphones—offers greater accessibility but introduces critical challenges related to computational constraints and privacy preservation. Performing segmentation directly on mobile edge devices avoids the need to transmit sensitive data to the cloud but requires models that are both lightweight and highly accurate. To this end, we propose SparseSegNet, an organically efficient segmentation framework that combines architectural simplicity with training-time innovations to enable real-time, on-device inference. SparseSegNet is built upon a Deep Layer Aggregation (DLA)-inspired encoder–decoder backbone, which effectively captures multi-scale lesion features while maintaining a compact model size. To further enhance boundary precision and generalization, we introduce a novel dual-teacher distillation strategy, termed Agreement-Guided Orthogonal Projection (AG-OP). This method transfers complementary spatial cues from two powerful vision foundation models— Segment Anything Model (SAM) based on Vision Transformer-Huge (ViT-H), and Segment Everything Everywhere Model (SEEM). Unlike traditional single-teacher distillation approaches, AG-OP encourages alignment between hard and soft pseudo-labels through orthogonal subspace projection, improving the robustness of the student model. We validate SparseSegNet across five public skin lesion segmentation benchmarks—ISIC 2017, ISIC 2018, PH$^2$, HAM10000, and Derm7pt —under a unified preprocessing and training pipeline. SparseSegNet achieves up to 0.91 Dice coefficient, 0.85 Intersection-over-Union (IoU), and 38ms latency with only 7 million parameters, outperforming recent compact models such as MobileSAM, CMUNeXt, and YOLOv8n-seg. Paired t-tests ($p < 0.01$) confirm the statistical significance of our improvements. Sparse