发表机构
Lawrence Technological University; College of Business and Information Technology, Lawrence Technological University（劳伦斯理工大学; 劳伦斯理工大学商学院与信息技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出DualMiT-Net双分支网络，结合局部MiT-B5编码器与全局EfficientNet-B5编码器的特征，在CBIS-DDSM数据集上实现了优于基线的乳腺肿块分割性能。
AI 中文摘要
乳腺肿块分割是计算机辅助乳腺X线摄影的重要步骤，但由于肿块对比度低、形状不规则且边界与周围乳腺组织融合，该任务仍具挑战性。为解决此问题，本文提出DualMiT-Net，这是一种双分支网络，同时利用肿块的聚焦视图和周围组织的宽视图。局部分支采用Mix Transformer（MiT-B5）编码器学习肿块的形状、纹理和边界信息，全局分支采用EfficientNet-B5编码器学习周围乳腺上下文。两个分支的特征在编码器较深层共享，随后在单个解码器中逐步融合，空间门控制解码过程中添加全局信息的量。本文还评估了四种输入表示，最终选择百分位数窗口化乳腺X线图像结合Gabor纹理响应的组合。该模型在数字乳腺X线筛查数据库的精选乳腺成像子集（CBIS-DDSM）的肿块子集上进行训练和评估，采用患者级划分。在三次训练运行中，采用指数移动平均权重的DualMiT-Net取得了0.9375的平均戴斯系数和0.8834的平均交并比，且在相同数据和训练设置下，其戴斯系数和交并比得分优于六个标准编码器-解码器基线。这些结果表明，将局部肿块信息与更宽的乳腺上下文相结合可实现准确且一致的乳腺肿块分割。
英文摘要
Breast mass segmentation is an important step in computer-aided mammography, but it remains difficult because masses can have low contrast, irregular shapes, and boundaries that blend with surrounding breast tissue. To address this problem, we present DualMiT-Net, a dual-branch network that uses both a focused view of the mass and a wider view of the surrounding tissue. The local branch uses a Mix Transformer (MiT-B5) encoder to learn mass shape, texture, and boundary information, while the global branch uses an EfficientNet-B5 encoder to learn surrounding breast context. Features from the two branches are shared at the deeper encoder levels and are then progressively fused in a single decoder. A spatial gate controls how much global information is added during decoding. We also evaluated four input representations and selected a percentile-windowed mammogram combined with a Gabor texture response. The model was trained and evaluated on the mass subset of the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) using a patient-level split. Across three training runs, DualMiT-Net with exponential moving average weights achieved a mean Dice coefficient of 0.9375 and a mean Intersection over Union of 0.8834. It also achieved better Dice and IoU scores than six standard encoder-decoder baselines trained using the same data and training settings. These results show that combining local mass information with wider breast context can provide accurate and consistent breast mass segmentation.
