用色彩编码提升乳腺钼靶图像可视性，助力医生更准判断密集型乳腺病变。
Visualizing the Invisible: Enhancing Radiologist Performance in Breast Mammography via Task-Driven Chromatic Encoding
- 通过任务驱动的色彩编码，将单通道钼靶图转为增强视觉表现的彩色图。
- 在致密乳腺中准确率提升显著，特异性达0.96，假阳性率明显降低。
- 适合放射科医生在乳腺癌筛查中减少漏诊与误判，尤其适用于致密乳腺人群。
乳腺钼靶筛查在致密型乳腺中敏感性较低，因组织重叠和微小病灶增加识别难度。本文提出MammoColor框架，结合轻量级任务驱动色彩编码（TDCE）模块，将单通道钼靶图像转换为增强视觉效果的彩色视图。该模型在VinDr-Mammo数据集上端到端训练，并在内部测试集、两个公开数据集（CBIS-DDSM、INBreast）及三个外部临床队列上评估性能。此外，进行了带洗脱期的多阅片者多病例（MRMC）研究，对比灰度图、仅使用TDCE编码图、以及灰度+TDCE并列显示三种条件。结果表明，在VinDr-Mammo上，模型将AUC从0.7669提升至0.8461（P=0.004），致密乳腺中提升更明显（AUC由0.749增至0.835）。在MRMC研究中，使用TDCE编码图使特异性从0.90提升至0.96（P=0.052），敏感性保持稳定。结论：TDCE提供了针对任务优化的色彩表示，可增强病灶感知显著性，有望减少乳腺筛查中的假阳性召回。
原文摘要 · Abstract (English)
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.
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