Abstract
Accurate segmentation of melanocytic tumors in dermoscopic images is a critical step for automated skin cancer screening and clinical decision support. Unlike natural scene segmentation, lesion delineation must reconcile subtle texture and color variations, frequent artifacts (hairs, rulers, bubbles), and a strong need for precise boundary localization to support downstream diagnosis. In this paper we introduce Our method, a novel ResNet inspired dual resolution architecture specifically designed for melanocytic tumor segmentation. Our method maintains a full resolution stream that preserves fine grained boundary information while a complementary pooled stream aggregates multi scale contextual cues for robust lesion recognition. The streams are tightly coupled by boundary aware residual connections that inject high frequency edge information into deep feature maps, and by a channel attention module that adapts color and texture sensitivity to dermoscopic appearance. To further address common imaging artifacts and the limited size of clinical datasets, we propose a lightweight artifact suppression block and a multi task training objective that combines a Dice Tversky segmentation loss with an explicit boundary loss and a contrastive regularizer for feature stability. The combined design yields pixel accurate masks without requiring heavy post processing or complex pre training protocols. Extensive experiments on public dermoscopic benchmarks demonstrate that Our method significantly improves boundary adherence and clinically relevant segmentation metrics compared to standard encoder decoder baselines, making it a practical building block for automated melanoma assessment systems.
Abstract (translated)
在皮肤癌筛查和临床决策支持中，对表皮黑色素肿瘤进行准确的分割是一个关键步骤。与自然场景分割不同的是，病变区域的边界划定必须解决细微的颜色和纹理变化、频繁出现的伪影（如毛发、尺子、气泡）以及精准定位边界的强烈需求问题，以支持后续诊断。本文介绍了我们提出的一种新的基于ResNet的双分辨率架构，专门用于黑色素肿瘤分割。 我们的方法保持了一条全分辨率流，该流保留了精细的边界信息，而另一条互补的池化流则通过汇集多尺度上下文线索来增强病变区域的稳健识别能力。这两股流通过边界感知残差连接紧密耦合在一起，这些连接将高频边缘信息注入到深层特征图中，并且使用一个通道注意力模块根据皮肤镜图像特性调整颜色和纹理敏感度。 为了进一步应对常见的成像伪影以及临床数据集规模较小的问题，我们提出了一种轻量级的伪影抑制块及一个多任务训练目标。该多任务训练目标结合了Dice-Tversky分割损失、明确的边界损失以及用于特征稳定性的对比正则化器。这种综合设计能够在不依赖重型后处理或复杂的预训练协议的情况下生成像素准确的掩模。 在公共皮肤镜基准测试中进行的广泛实验表明，我们的方法相较于标准的编码解码基线模型，在边界贴合度和临床上相关的分割指标方面有了显著提高，使其成为自动黑色素瘤评估系统中的实用构建模块。
URL
https://arxiv.org/abs/2508.06816
