MedLiteNet: Lightweight Hybrid Medical Image Segmentation Model
This work addresses the challenge of efficient and precise medical image segmentation for dermatology, though it appears incremental as it builds on existing CNN and Transformer methods.
The paper tackled the problem of accurate skin-lesion segmentation for computer-aided diagnosis of skin cancer by introducing MedLiteNet, a lightweight CNN-Transformer hybrid model that achieves high precision through hierarchical feature extraction and multi-scale context aggregation.
Accurate skin-lesion segmentation remains a key technical challenge for computer-aided diagnosis of skin cancer. Convolutional neural networks, while effective, are constrained by limited receptive fields and thus struggle to model long-range dependencies. Vision Transformers capture global context, yet their quadratic complexity and large parameter budgets hinder use on the small-sample medical datasets common in dermatology. We introduce the MedLiteNet, a lightweight CNN Transformer hybrid tailored for dermoscopic segmentation that achieves high precision through hierarchical feature extraction and multi-scale context aggregation. The encoder stacks depth-wise Mobile Inverted Bottleneck blocks to curb computation, inserts a bottleneck-level cross-scale token-mixing unit to exchange information between resolutions, and embeds a boundary-aware self-attention module to sharpen lesion contours.