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.
Abstract (translated)
准确的皮肤病变分割仍然是计算机辅助诊断皮肤癌的关键技术挑战。卷积神经网络虽然有效，但由于有限的感受野而难以建模长距离依赖关系。视觉变换器能够捕捉全局上下文，但其二次复杂度和庞大的参数预算阻碍了在常见于皮肤病学的小样本医疗数据集上的应用。我们引入了MedLiteNet，这是一种轻量级的CNN-Transformer混合模型，专门用于皮肤镜检查分割，并通过分层特征提取和多尺度上下文聚合实现了高精度。编码器堆叠深度方向的Mobile Inverted Bottleneck块以减少计算成本，在瓶颈级别插入跨尺度令牌混合单元以便在不同分辨率之间交换信息，并嵌入一个边缘感知自注意力模块来锐化病变轮廓。
URL
https://arxiv.org/abs/2509.03041
