AI 中文总结
提出CLC-YOLO，通过通道门控局部高通残差改进YOLO26分割头，在四个乳腺超声数据集上提升组宏Dice，且开销极小。
AI 中文摘要
可靠的乳腺超声病变分割需要准确的边界以及防止患者或重复图像跨越数据划分的评估。我们提出了通道局部对比（Channel Local Contrast, CLC），这是对YOLO26分割原型头的紧凑改进。CLC添加了一个由64个零初始化、有界通道门控制的固定局部高通残差。在四个乳腺超声数据集上，分别在五个匹配折中比较了基线和CLC。BUS-BRA使用患者不重叠的外部测试和单独的内部验证。BUS-UCLM和BrEaST使用患者分组验证折；BUSI使用重复组件组，因为患者标识符不可用。组宏Dice在BUS-BRA、BUS-UCLM、BUSI和BrEaST上分别提高了1.68、3.11、1.12和2.45个百分点。只有BUS-BRA的配对95%置信区间排除了零。CLC增加了64个参数和0.0049千兆浮点运算（GFLOPs）。在640像素下，单T4、批大小为一、16位浮点（FP16）TensorRT图时间，CLC为2.1478毫秒，基线为2.0512毫秒，不包括预处理和后处理。CLC在所有四个数据集上提高了组宏Dice，实测T4前向传播开销为0.0966毫秒。代码：此https URL
英文摘要
Reliable breast ultrasound lesion segmentation requires accurate boundaries and evaluation that prevents patients or duplicate images from crossing data splits. We propose Channel Local Contrast (CLC), a compact refinement of the YOLO26 segmentation prototype head. CLC adds a fixed local high-pass residual controlled by 64 zero-initialized, bounded channel gates. Baseline and CLC were compared in five matched folds on each of four breast ultrasound datasets. BUS-BRA used patient-disjoint outer tests with separate inner validation. BUS-UCLM and BrEaST used patient-grouped validation folds; BUSI used duplicate-component groups because patient identifiers are unavailable. Group-macro Dice increased by 1.68, 3.11, 1.12, and 2.45 percentage points on BUS-BRA, BUS-UCLM, BUSI, and BrEaST, respectively. Only the BUS-BRA paired 95% confidence interval excluded zero. CLC adds 64 parameters and 0.0049 giga floating-point operations (GFLOPs). At 640 pixels, single-T4, batch-one, 16-bit floating-point (FP16) TensorRT graph times were 2.1478 ms for CLC and 2.0512 ms for baseline, excluding preprocessing and postprocessing. CLC increased group-macro Dice across all four datasets with a measured T4 forward-pass overhead of 0.0966 ms. Code: https://github.com/mfazrinizar/CLC-YOLO
CommentsAccepted to IEEE ICAITech 2026
