📄 中文摘要
该研究提出了一种基于机器学习的乳腺癌筛查工作流程优化方法。通过分析大量的医学影像数据，模型能够提高筛查的准确性和效率，减少假阳性和假阴性结果的发生。研究中采用了先进的深度学习技术，结合临床数据和影像特征，显著提升了乳腺癌早期检测的能力。此外，研究还探讨了如何将该技术集成到现有的医疗系统中，以便于医生在实际工作中使用，从而提高患者的诊断体验和治疗效果。该方法为未来乳腺癌筛查提供了新的思路和方向。
利用机器学习改善乳腺癌筛查工作流程
出处: Improving breast cancer screening workflows with machine learning
发布: 2026年3月17日
📄 English Summary
Improving breast cancer screening workflows with machine learning
This research presents a machine learning-based approach to optimize breast cancer screening workflows. By analyzing large volumes of medical imaging data, the model enhances the accuracy and efficiency of screenings, reducing the occurrence of false positives and false negatives. Advanced deep learning techniques are employed, integrating clinical data with imaging features to significantly improve early detection capabilities for breast cancer. Additionally, the study explores the integration of this technology into existing healthcare systems, facilitating its practical use by physicians and thereby enhancing patient diagnosis and treatment experiences. This approach offers new insights and directions for future breast cancer screening.