基于深度学习的色彩迁移生物医学成像技术

传统病理学检测中，由于复杂的染色流程和单一的观察形式等限制着病情的诊断速度，而染色过程实质上是将颜色信息与形态特征关联，效果等同于现代数字技术的生物医学图像的图义分割，这使得研究者们可以通过计算后处理的方式，大大降低生物医学成像处理样品的步骤，实现与传统医学染色金标准一致的成像效果。近些年人工智能深度学习领域的发展促成了计算机辅助分析领域与临床医疗的有效结合，人工智能色彩迁移技术在生物医学成像分析上也逐渐表现出较高的发展潜力。文中回顾了深度学习色彩迁移的技术原理，列举此类技术在生物医学成像领域中的部分应用，并展望了人工智能色彩迁移在生物医学成像领域的研究现状和可能的发展趋势。

Deep learning-based color transfer biomedical imaging technology

In traditional pathology detection, the speed of diagnosis is limited due to the complex staining process and single observation form. The staining process is essentially associating color information with morphological features, and the effect is equivalent to that of biomedical images of modern digital technology. Sense segmentation, which allows researchers to greatly reduce the steps of biomedical imaging processing samples through computational post-processing, and achieve imaging results consistent with the gold standard of traditional medical staining. In recent years, the development of artificial intelligence deep learning has contributed to the effective combination of computer-aided analysis and clinical medicine, and artificial intelligence color transfer technology has gradually shown high development potential in biomedical imaging analysis. This paper will review the technical principles of deep learning color transfer, enumerate some applications of such technologies in the field of biomedical imaging, and look forward to the research status and possible development trends of artificial intelligence color transfer in the field of biomedical imaging.