MedLiteNet: Lightweight Hybrid Medical Image Segmentation Model

• URL: http://arxiv.org/abs/2509.03041v1
• Date: Wed, 03 Sep 2025 05:59:13 GMT
• Title: MedLiteNet: Lightweight Hybrid Medical Image Segmentation Model
• Authors: Pengyang Yu, Haoquan Wang, Gerard Marks, Tahar Kechadi, Laurence T. Yang, Sahraoui Dhelim, Nyothiri Aung,
• Abstract summary: We introduce the MedLiteNet, a lightweight CNN Transformer hybrid tailored for dermoscopic segmentation.<n>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.
• Score: 17.73370811236741
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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