Purpose:Mammography screening is less sensitive in dense breasts, where tissue overlap and subtle findings increase perceptual difficulty. We present MammoColor, an end-to-end framework with a Task-Driven Chromatic Encoding (TDCE) module that converts single-channel mammograms into TDCE-encoded views for visual augmentation. Materials and Methods:MammoColor couples a lightweight TDCE module with a BI-RADS triage classifier and was trained end-to-end on VinDr-Mammo. Performance was evaluated on an internal test set, two public datasets (CBIS-DDSM and INBreast), and three external clinical cohorts. We also conducted a multi-reader, multi-case (MRMC) observer study with a washout period, comparing (1) grayscale-only, (2) TDCE-only, and (3) side-by-side grayscale+TDCE. Results:On VinDr-Mammo, MammoColor improved AUC from 0.7669 to 0.8461 (P=0.004). Gains were larger in dense breasts (AUC 0.749 to 0.835). In the MRMC study, TDCE-encoded images improved specificity (0.90 to 0.96; P=0.052) with comparable sensitivity. Conclusion:TDCE provides a task-optimized chromatic representation that may improve perceptual salience and reduce false-positive recalls in mammography triage.

翻译：目的：在致密型乳腺中，钼靶筛查的敏感性较低，因为组织重叠和细微发现增加了感知难度。我们提出了MammoColor，这是一个包含任务驱动色彩编码（TDCE）模块的端到端框架，可将单通道钼靶图像转换为TDCE编码视图以进行视觉增强。材料与方法：MammoColor将一个轻量级TDCE模块与BI-RADS分诊分类器耦合，并在VinDr-Mammo数据集上进行了端到端训练。性能在内部测试集、两个公共数据集（CBIS-DDSM和INBreast）以及三个外部临床队列中进行了评估。我们还进行了一项包含洗脱期的多阅片者多病例（MRMC）观察者研究，比较了（1）仅灰度图，（2）仅TDCE编码图，以及（3）并排显示的灰度图+TDCE编码图。结果：在VinDr-Mammo上，MammoColor将AUC从0.7669提高到0.8461（P=0.004）。在致密型乳腺中增益更大（AUC从0.749提高到0.835）。在MRMC研究中，TDCE编码图像在敏感性相当的情况下提高了特异性（从0.90到0.96；P=0.052）。结论：TDCE提供了一种任务优化的色彩表示，可能提高钼靶分诊中的感知显著性并减少假阳性召回。