轻量级混合模型提升皮肤病变分割精度
MedLiteNet: Lightweight Hybrid Medical Image Segmentation Model
- 融合卷积与注意力机制，分层提取特征并跨尺度传递信息
- 在ISIC-2018数据集上达到93.6%的分割精度，参数量仅0.8M
- 适合资源受限场景，尤其适用于小样本皮肤病图像分析
皮肤病变精准分割是皮肤癌辅助诊断的关键技术挑战。尽管卷积神经网络有效，但其感受野有限，难以建模长距离依赖；视觉变换器虽能捕捉全局上下文，却因二次复杂度和庞大的参数量，在皮肤科常见的小样本医疗数据集上应用受限。本文提出MedLiteNet，一种专为皮肤镜图像分割设计的轻量化CNN-Transformer混合模型，通过分层特征提取与多尺度上下文聚合实现高精度分割。编码器采用深度可分离的Mobile Inverted Bottleneck块以降低计算开销，插入瓶颈级跨尺度令牌混合单元以实现不同分辨率间的信息交换，并嵌入边界感知自注意力模块以锐化病变轮廓。
原文摘要 · Abstract (English)
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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